EurekaMag PDF full texts Chapter 58,594

Basques, B.A.; Ibe, I.; Samuel, A.M.; Lukasiewicz, A.M.; Webb, M.L.; Bohl, D.D.; Grauer, J.N. 2017: Predicting Postoperative Morbidity and Readmission for Revision Posterior Lumbar Fusion. Clinical Spine Surgery 30(6): E770-E775

Sieberg, C.B.; Manganella, J.; Manalo, G.; Simons, L.E.; Hresko, M.T. 2017: Predicting Postsurgical Satisfaction in Adolescents with Idiopathic Scoliosis: the Role of Presurgical Functioning and Expectations. Journal of Pediatric Orthopedics 37(8): E548-E551

Bautista, I.J.; Chirosa, I.J.; Tamayo, I.M.ín.; González, A.és.; Robinson, J.E.; Chirosa, L.J.; Robertson, R.J. 2014: Predicting Power Output of Upper Body using the OMNI-RES Scale. Journal of Human Kinetics 44: 161-169

Mehra, L.K.; Cowger, C.; Gross, K.; Ojiambo, P.S. 2016: Predicting Pre-planting Risk of Stagonospora nodorum blotch in Winter Wheat Using Machine Learning Models. Frontiers in Plant Science 7: 390

Tanaka, N.; Ohno, Y.; Hori, M.; Utada, M.; Ito, K.; Suzuki, T.; Furukawa, F. 2015: Predicting Preoperative Hemodynamic Changes Using the Visual Analog Scale. Journal of Perianesthesia Nursing: Official Journal of the American Society of Perianesthesia Nurses 30(6): 460-467

Miller, N.; Frankenfield, D.; Lehman, E.; Maguire, M.; Schirm, V. 2016: Predicting Pressure Ulcer Development in Clinical Practice: Evaluation of Braden Scale Scores and Nutrition Parameters. Journal of Wound Ostomy and Continence Nursing: Official Publication of Wound Ostomy and Continence Nurses Society 43(2): 133-139

Tucker, C.M.; Berrien, K.; Menard, M.Kathryn.; Herring, A.H.; Daniels, J.; Rowley, D.L.; Halpern, C.Tucker. 2015: Predicting Preterm Birth Among Women Screened by North Carolina's Pregnancy Medical Home Program. Maternal and Child Health Journal 19(11): 2438-2452

Georgiou, H.M.; Di Quinzio, M.K.W.; Permezel, M.; Brennecke, S.P. 2015: Predicting Preterm Labour: Current Status and Future Prospects. Disease Markers 2015: 435014

Eggers, S.M.; Mathews, C.; Aarø, L.E.; McClinton-Appollis, T.; Bos, A.E.R.; de Vries, H. 2017: Predicting Primary and Secondary Abstinence Among Adolescent Boys and Girls in the Western Cape, South Africa. Aids and Behavior 21(5): 1417-1428

Dyrba, M.; Barkhof, F.; Fellgiebel, A.; Filippi, M.; Hausner, L.; Hauenstein, K.; Kirste, T.; Teipel, S.J. 2015: Predicting Prodromal Alzheimer's Disease in Subjects with Mild Cognitive Impairment Using Machine Learning Classification of Multimodal Multicenter Diffusion-Tensor and Magnetic Resonance Imaging Data. Journal of Neuroimaging: Official Journal of the American Society of Neuroimaging 25(5): 738-747

Lauer, M.S.; Danthi, N.S.; Kaltman, J.; Wu, C. 2015: Predicting Productivity Returns on Investment: Thirty Years of Peer Review, Grant Funding, and Publication of Highly Cited Papers at the National Heart, Lung, and Blood Institute. Circulation research 117(3): 239-243

Condon, J.V.; Barefield, A.C. 2016: Predicting Professional Examination Outcomes: a Case of the Registered Health Information Administration Certification Examination. Journal of Allied Health 45(4): 267-273

Waters, D.D.; Arsenault, B.J. 2016: Predicting Prognosis in Acute Coronary Syndromes: Makeover time for TIMi and GRACE?. Canadian Journal of Cardiology 32(11): 1290-1293

Nabhan, C.; Raca, G.; Wang, Y.L. 2015: Predicting Prognosis in Chronic Lymphocytic Leukemia in the Contemporary Era. JAMA Oncology 1(7): 965-974

Der, S.; Zhu, C.-Q.; Brower, S.; Uihlein, A. 2015: Predicting Prognosis of Early-Stage Non-Small Cell Lung Cancer Using the GeneFx® Lung Signature. Plos Currents 7

Korolev, I.O.; Symonds, L.L.; Bozoki, A.C. 2016: Predicting Progression from Mild Cognitive Impairment to Alzheimer's Dementia Using Clinical, MRI, and Plasma Biomarkers via Probabilistic Pattern Classification. Plos one 11(2): E0138866

Kadatz, M.J.; Lee, E.S.; Levin, A. 2016: Predicting Progression in CKD: Perspectives and Precautions. American Journal of Kidney Diseases: the Official Journal of the National Kidney Foundation 67(5): 779-786

Cideciyan, A.V.; Swider, M.; Schwartz, S.B.; Stone, E.M.; Jacobson, S.G. 2015: Predicting Progression of ABCA4-Associated Retinal Degenerations Based on Longitudinal Measurements of the Leading Disease Front. Investigative Ophthalmology and Visual Science 56(10): 5946-5955

Tsai, C.; Latimer, A.A.; Yoo, J.S.; Studt, F.; Abild-Pedersen, F. 2015: Predicting Promoter-Induced Bond Activation on Solid Catalysts Using Elementary Bond Orders. Journal of Physical Chemistry Letters 6(18): 3670-3674

Zhang, K.; Shen, Y.; Zhang, X.; Ma, L.; Wang, H.; An, N.; Guo, A.; Ye, H. 2016: Predicting Prostate Biopsy Outcomes: a Preliminary Investigation on Screening with Ultrahigh B-Value Diffusion-Weighted Imaging as an Innovative Diagnostic Biomarker. Plos one 11(3): E0151176

Albitar, M.; Ma, W.; Lund, L.; Albitar, F.; Diep, K.; Fritsche, H.A.; Shore, N. 2016: Predicting Prostate Biopsy Results Using a Panel of Plasma and Urine Biomarkers Combined in a Scoring System. Journal of Cancer 7(3): 297-303

Jeffers, A.; Sochat, V.; Kattan, M.W.; Yu, C.; Melcon, E.; Yamoah, K.; Rebbeck, T.R.; Whittemore, A.S. 2017: Predicting Prostate Cancer Recurrence After Radical Prostatectomy. Prostate 77(3): 291-298

Yu, G.; Rangwala, H.; Domeniconi, C.; Zhang, G.; Zhang, Z. 2015: Predicting Protein Function Using Multiple Kernels. Ieee/Acm Transactions on Computational Biology and Bioinformatics 12(1): 219-233

Yu, G.; Fu, G.; Wang, J.; Zhu, H. 2016: Predicting Protein Function via Semantic Integration of Multiple Networks. Ieee/Acm Transactions on Computational Biology and Bioinformatics 13(2): 220-232

Deng, X.; Li, J.; Cheng, J. 2013: Predicting Protein Model Quality from Sequence Alignments by Support Vector Machines. Journal of Proteomics and Bioinformatics Suppl. 9

García-Jiménez, B.; Pons, T.; Sanchis, A.; Valencia, A. 2014: Predicting Protein Relationships to Human Pathways through a Relational Learning Approach Based on Simple Sequence Features. Ieee/Acm Transactions on Computational Biology and Bioinformatics 11(4): 753-765

Kandoi, G.; Leelananda, S.P.; Jernigan, R.L.; Sen, T.Z. 2017: Predicting Protein Secondary Structure Using Consensus Data Mining (CDM) Based on Empirical Statistics and Evolutionary Information. Methods in Molecular Biology 1484: 35-44

Hu, J.; Li, Y.; Zhang, M.; Yang, X.; Shen, H-Bin.; Yu, D-Jun. 2017: Predicting Protein-DNA Binding Residues by Weightedly Combining Sequence-Based Features and Boosting Multiple SVMs. Ieee/Acm Transactions on Computational Biology and Bioinformatics 14(6): 1389-1398

Kuo, T.-H.; Li, K.-B. 2016: Predicting Protein-Protein Interaction Sites Using Sequence Descriptors and Site Propensity of Neighboring Amino Acids. International Journal of Molecular Sciences 17(11)

Sriwastava, B.K.; Basu, S.; Maulik, U. 2015: Predicting Protein-Protein Interaction Sites with a Novel Membership Based Fuzzy SVM Classifier. IEEE/ACM transactions on computational biology and bioinformatics 12(6): 1394-1404

Almeida, R.M.; Dell'Acqua, S.; Krippahl, L.; Moura, J.é J.G.; Pauleta, S.R. 2016: Predicting Protein-Protein Interactions Using BiGGER: Case Studies. Molecules 21(8)

Keskin, O.; Tuncbag, N.; Gursoy, A. 2016: Predicting Protein-Protein Interactions from the Molecular to the Proteome Level. Chemical Reviews 116(8): 4884-4909

Paschoal, D.; Guerra, C.F.; de Oliveira, M.A.L.; Ramalho, T.C.; Dos Santos, H.F. 2016: Predicting Pt-195 NMR chemical shift using new relativistic all-electron basis set. Journal of Computational Chemistry 37(26): 2360-2373

Heinzerling, L.; Hartmann, R.W.; Frotscher, M.; Neumann, D. 2010: Predicting Putative Inhibitors of 17β-HSD1. Molecular Informatics 29(10): 695-705

Herrera, S.; Reyes-Herrera, P.H.; Shank, T.M. 2015: Predicting RAD-seq Marker Numbers across the Eukaryotic Tree of Life. Genome Biology and Evolution 7(12): 3207-3225

Kerpedjiev, P.; Höner Zu Siederdissen, C.; Hofacker, I.L. 2015: Predicting RNA 3D structure using a coarse-grain helix-centered model. Rna 21(6): 1110-1121

Sakuraba, S.; Asai, K.; Kameda, T. 2015: Predicting RNA Duplex Dimerization Free-Energy Changes upon Mutations Using Molecular Dynamics Simulations. Journal of Physical Chemistry Letters 6(21): 4348-4351

Lorenz, R.; Wolfinger, M.T.; Tanzer, A.; Hofacker, I.L. 2016: Predicting RNA secondary structures from sequence and probing data. Methods 103: 86-98

DiChiacchio, L.; Mathews, D.H. 2016: Predicting RNA-RNA Interactions Using RNAstructure. Methods in Molecular Biology 1490: 51-62

Mehmood, Q.; Sun, A.; Becker, N.; Higgins, J.; Marshall, A.; Le, L.W.; Vines, D.C.; McCloskey, P.; Ford, V.; Clarke, K.; Yap, M.; Bezjak, A.; Bissonnette, J.-P. 2016: Predicting Radiation Esophagitis Using 18F-FDG PET During Chemoradiotherapy for Locally Advanced Non-Small Cell Lung Cancer. Journal of Thoracic Oncology: Official Publication of the International Association for the Study of Lung Cancer 11(2): 213-221

Van Oorschot, B.; Hovingh, S.; Dekker, A.; Stalpers, L.J.; Franken, N.A.P. 2016: Predicting Radiosensitivity with Gamma-H2AX Foci Assay after Single High-Dose-Rate and Pulsed Dose-Rate Ionizing Irradiation. Radiation Research 185(2): 190-198

Feng, Z.; Xu, Q.S.; Qin, L.Z.; Li, H.; Han, Z. 2017: Predicting Radiotherapy Necessity in Tongue Cancer Using Lymph Node Yield. Journal of Oral and Maxillofacial Surgery: Official Journal of the American Association of Oral and Maxillofacial Surgeons 75(5): 1062-1070

Chaudhuri, A.A.; Binkley, M.S.; Osmundson, E.C.; Alizadeh, A.A.; Diehn, M. 2015: Predicting Radiotherapy Responses and Treatment Outcomes Through Analysis of Circulating Tumor DNA. Seminars in Radiation Oncology 25(4): 305-312

Osman, S.L. 2016: Predicting Rape Victim Empathy Based on Rape Victimization and Acknowledgment Labeling. Violence Against Women 22(7): 767-779

AbdulHameed, M.D.M.; Ippolito, D.L.; Wallqvist, A. 2016: Predicting Rat and Human Pregnane X Receptor Activators Using Bayesian Classification Models. Chemical Research in Toxicology 29(10): 1729-1740

Messner, S.F.; Liu, J.; Zhao, Y. 2018: Predicting Re-Incarceration Status of Prisoners in Contemporary China: Applying Western Criminological Theories. International Journal of Offender Therapy and Comparative Criminology 62(4): 1018-1042

Zhao, M.; Anderson, A.B. 2016: Predicting Reaction Mechanisms and Potentials in Acid and Base from Self-Consistent Quantum Theory: H(ads) and OH(ads) Deposition on the Pt(111) Electrode. Journal of Physical Chemistry Letters 7(4): 711-714

Michaels, J.A.; Dann, B.; Intveld, R.W.; Scherberger, H.ör. 2015: Predicting Reaction time from the Neural State Space of the Premotor and Parietal Grasping Network. Journal of Neuroscience: the Official Journal of the Society for Neuroscience 35(32): 11415-11432

Bigozzi, L.; Tarchi, C.; Caudek, C.; Pinto, G. 2016: Predicting Reading and Spelling Disorders: a 4-Year Prospective Cohort Study. Frontiers in Psychology 7: 337

Pinto, G.; Bigozzi, L.; Tarchi, C.; Vezzani, C.; Accorti Gamannossi, B. 2016: Predicting Reading, Spelling, and Mathematical Skills: a Longitudinal Study from Kindergarten Through first Grade. Psychological Reports 118(2): 413-440

Tabak, Y.P.; Sun, X.; Nunez, C.M.; Gupta, V.; Johannes, R.S. 2017: Predicting Readmission at Early Hospitalization Using Electronic Clinical Data: An Early Readmission Risk Score. Medical Care 55(3): 267-275

Peterson, L.; Jamroz, M.; Kolinski, A.; Kihara, D. 2017: Predicting Real-Valued Protein Residue Fluctuation Using FlexPred. Methods in Molecular Biology 1484: 175-186

Tesoriero, A.J.; Terziotti, S.; Abrams, D.B. 2015: Predicting Redox Conditions in Groundwater at a Regional Scale. Environmental Science and Technology 49(16): 9657-9664

Lowe, J.A.; Crist, B.D.; Pfeiffer, F.; Carson, W.L. 2015: Predicting Reduction in Torsional Strength by Concentric/Eccentric RIA Reaming Normal and Osteoporotic Long Bones (Femurs). Journal of Orthopaedic Trauma 29(10): E371-E379

Mattsson, N.; Insel, P.S.; Donohue, M.; Jagust, W.; Sperling, R.; Aisen, P.; Weiner, M.W. 2015: Predicting Reduction of Cerebrospinal Fluid β-Amyloid 42 in Cognitively Healthy Controls. JAMA Neurology 72(5): 554-560

Almquist, Z.W.; Butts, C.T. 2015: Predicting Regional Self-identification from Spatial Network Models. Geographical Analysis 47(1): 50-72

Tyzack, J.D.; Hunt, P.A.; Segall, M.D. 2016: Predicting Regioselectivity and Lability of Cytochrome P450 Metabolism Using Quantum Mechanical Simulations. Journal of Chemical Information and Modeling 56(11): 2180-2193

Almutairi, K.M. 2016: Predicting Relationship of Smoking Behavior Among Male Saudi Arabian College Students Related to Their Religious Practice. Journal of religion and health 55(2): 469-479

Norouzi, J.; Yadollahpour, A.; Mirbagheri, S.A.; Mazdeh, M.M.; Hosseini, S.A. 2016: Predicting Renal Failure Progression in Chronic Kidney Disease Using Integrated Intelligent Fuzzy Expert System. Computational and Mathematical Methods in Medicine 2016: 6080814

Meyer, A.; Woldu, S.L.; Weinberg, A.C.; Thoreson, G.R.; Pierorazio, P.; Matulay, J.T.; Benson, M.C.; DeCastro, G.J.; McKiernan, J.M. 2015: Predicting Renal Parenchymal Loss after Nephron Sparing Surgery. Journal of Urology 194(3): 658-663

Hawkins, N.J.; Fraaije, B.A. 2016: Predicting Resistance by Mutagenesis: Lessons from 45 Years of MBC Resistance. Frontiers in microbiology 7: 1814

Segal, O.; Barayev, E.; Nemet, A.Y.; Mimouni, M. 2016: Predicting Response of Exudative Age-Related Macular Degeneration to Bevacizumab Based on Spectralis Optical Coherence Tomography. Retina 36(2): 259-263

Santacana, Mí.; Arias, Bárbara.; Mitjans, M.; Bonillo, A.; Montoro, Mía.; Rosado, Sílvia.; Guillamat, R.; Vallès, Vç.; Pérez, Víctor.; Forero, C.G.; Fullana, M.A. 2016: Predicting Response Trajectories during Cognitive-Behavioural Therapy for Panic Disorder: No Association with the BDNF Gene or Childhood Maltreatment. Plos one 11(6): E0158224

Geeleher, P.; Loboda, A.; Lenkala, D.; Wang, F.; LaCroix, B.; Karovic, S.; Wang, J.; Nebozhyn, M.; Chisamore, M.; Hardwick, J.; Maitland, M.L.; Huang, R.S. 2015: Predicting Response to Histone Deacetylase Inhibitors Using High-Throughput Genomics. Journal of the National Cancer Institute 107(11)

Zuiverloon, T.C.M.; Zwarthoff, E.C. 2016: Predicting Response to Intravesical Bacillus Calmette-Guérin Immunotherapy: Are we Moving Forward?. European Urology 69(2): 201-202

Ypsilantis, P.-P.; Siddique, M.; Sohn, H.-M.; Davies, A.; Cook, G.; Goh, V.; Montana, G. 2015: Predicting Response to Neoadjuvant Chemotherapy with PET Imaging Using Convolutional Neural Networks. Plos one 10(9): E0137036

Gammaitoni, A.R.; Trudeau, J.J.; Radnovich, R.; Galer, B.S.; Jensen, M.P. 2015: Predicting Response to Subacromial Injections and Lidocaine/Tetracaine Patch from Pretreatment Pain Quality in Patients with Shoulder Impingement Syndrome. Pain Medicine 16(7): 1333-1340

Hill, D.A.; Lim, H.-W.; Kim, Y.H.; Ho, W.Y.; Foong, Y.H.; Nelson, V.L.; Nguyen, H.C.B.; Chegireddy, K.; Kim, J.; Habertheuer, A.; Vallabhajosyula, P.; Kambayashi, T.; Won, K.-J.; Lazar, M.A. 2018: Distinct macrophage populations direct inflammatory versus physiological changes in adipose tissue. Proceedings of the National Academy of Sciences of the United States of America 115(22): E5096-E5105

Zhang, J.; Zhang, Z.; Zhang, Y.; Wu, Y. 2018: Distinct magnetic resonance imaging features in a patient with novel RARS2 mutations: A case report and review of the literature. Experimental and Therapeutic Medicine 15(1): 1099-1104

Akbulut, V.; Weger, H. 2016: Predicting Responses to Bids for Sexual and Romantic Escalation in Cross-Sex Friendships. Journal of Social Psychology 156(1): 98-114

Tromberg, B.J.; Zhang, Z.; Leproux, A.ïs.; O'Sullivan, T.D.; Cerussi, A.E.; Carpenter, P.M.; Mehta, R.S.; Roblyer, D.; Yang, W.; Paulsen, K.D.; Pogue, B.W.; Jiang, S.; Kaufman, P.A.; Yodh, A.G.; Chung, S.H.; Schnall, M.; Snyder, B.S.; Hylton, N.; Boas, D.A.; Carp, S.A.; Isakoff, S.J.; Mankoff, D. 2016: Predicting Responses to Neoadjuvant Chemotherapy in Breast Cancer: ACRIN 6691 Trial of Diffuse Optical Spectroscopic Imaging. Cancer Research 76(20): 5933-5944

Nozaki, T.; Tasaki, A.; Horiuchi, S.; Ochi, J.; Starkey, J.; Hara, T.; Saida, Y.; Yoshioka, H. 2016: Predicting Retear after Repair of Full-Thickness Rotator Cuff Tear: Two-Point Dixon MR Imaging Quantification of Fatty Muscle Degeneration-Initial Experience with 1-year Follow-up. Radiology 280(2): 500-509

Martin, D.M.; Gálvez, V.òn.; Loo, C.K. 2015: Predicting Retrograde Autobiographical Memory Changes Following Electroconvulsive Therapy: Relationships between Individual, Treatment, and Early Clinical Factors. International Journal of Neuropsychopharmacology 18(12)

Kim, E.; Hou, J.; Han, J.Yeob.; Himelboim, I. 2016: Predicting Retweeting Behavior on Breast Cancer Social Networks: Network and Content Characteristics. Journal of Health Communication 21(4): 479-486

Lancellotti, P.; Moonen, M.; Jerusalem, G. 2016: Predicting Reversibility of Anticancer Drugs-Related Cardiac Dysfunction: a new Piece to the Routine use of Deformation Imaging. Echocardiography 33(4): 504-509

Gaspar, M.P.; Kane, P.M.; Putthiwara, D.; Jacoby, S.M.; Osterman, A.Lee. 2016: Predicting Revision Following In Situ Ulnar Nerve Decompression for Patients With Idiopathic Cubital Tunnel Syndrome. Journal of Hand Surgery 41(3): 427-435

Guo, H. 2017: Predicting Rights-Only Score Distributions from Data Collected Under Formula Score Instructions. Psychometrika 82(1): 1-16

Hachamovitch, R.; Nutter, B.; Menon, V.; Cerqueira, M.D. 2015: Predicting Risk Versus Predicting Potential Survival Benefit Using 123I-mIBG Imaging in Patients with Systolic Dysfunction Eligible for Implantable Cardiac Defibrillator Implantation: Analysis of Data from the Prospective ADMIRE-HF Study. CIRCULATION. Cardiovascular Imaging 8(12)

Seimiya, H. 2015: Predicting Risk at the End of the End: Telomere G-tail as a Biomarker. Ebiomedicine 2(8): 804-805

Brackman, E.H.; Morris, B.W.; Andover, M.S. 2016: Predicting Risk for Suicide: A Preliminary Examination of Non-Suicidal Self-Injury and the Acquired Capability Construct in a College Sample. Archives of suicide research: official journal of the International Academy for Suicide Research 20(4): 663-676

Ramirez, M.E.; Bargman, J. 2015: Predicting Risk in Peritoneal Dialysis: Is Membrane Biology Destiny?. Clinical Journal of the American Society of Nephrology: Cjasn 10(11): 1895-1896

Odeh, R.; Noone, D.; Bowlin, P.R.; Braga, L.H.P.; Lorenzo, A.J. 2016: Predicting Risk of Chronic Kidney Disease in Infants and Young Children at Diagnosis of Posterior Urethral Valves: Initial Ultrasound Kidney Characteristics and Validation of Parenchymal Area as Forecasters of Renal Reserve. Journal of Urology 196(3): 862-868

Ross, S.S. 2016: Predicting Risk of Chronic Renal Disease in Children with Vesicoureteral Reflux-How Good or Bad are we Doing?. Journal of Urology 195(4 Part 1): 829-830

Harrison, S.L.; de Craen, A.J.M.; Kerse, N.; Teh, R.; Granic, A.; Davies, K.; Wesnes, K.A.; den Elzen, W.P.J.; Gussekloo, J.; Kirkwood, T.B.L.; Robinson, L.; Jagger, C.; Siervo, M.; Stephan, B.C.M. 2017: Predicting Risk of Cognitive Decline in very Old Adults Using Three Models: the Framingham Stroke Risk Profile; the Cardiovascular Risk Factors, Aging, and Dementia Model; and Oxi-Inflammatory Biomarkers. Journal of the American Geriatrics Society 65(2): 381-389

Tonolini, M.; Rigiroli, F.; Scorza, D. 2016: Predicting Risk of Contrast-Induced Nephrotoxicity in Hospitalized Patients Undergoing Computed Tomography Using the Mehran Stratification Score. Current Problems in Diagnostic Radiology 45(3): 238-239

Lo Re, V.; Kallan, M.J.; Tate, J.P.; Lim, J.K.; Goetz, M.B.; Klein, M.B.; Rimland, D.; Rodriguez-Barradas, M.C.; Butt, A.A.; Gibert, C.L.; Brown, S.T.; Park, L.S.; Dubrow, R.; Reddy, K.R.; Kostman, J.R.; Justice, A.C.; Localio, A.R. 2015: Predicting Risk of End-Stage Liver Disease in Antiretroviral-Treated Human Immunodeficiency Virus/Hepatitis C Virus-Coinfected Patients. Open Forum Infectious Diseases 2(3): Ofv109

Palraj, B.R.; Baddour, L.M.; Hess, E.P.; Steckelberg, J.M.; Wilson, W.R.; Lahr, B.D.; Sohail, M.R. 2015: Predicting Risk of Endocarditis Using a Clinical Tool (PREDICT): Scoring System to Guide use of Echocardiography in the Management of Staphylococcus aureus Bacteremia. Clinical Infectious Diseases: An Official Publication of the Infectious Diseases Society of America 61(1): 18-28

Mehran, R.; Baber, U. 2016: Predicting Risk of Ischemic or Bleeding Events After Percutaneous Coronary Intervention. JAMA Cardiology 1(6): 731-732

Gracitelli, C.P.B.; Tatham, A.J.; Boer, E.R.; Abe, R.Y.; Diniz-Filho, A.; Rosen, P.N.; Medeiros, F.A. 2015: Predicting Risk of Motor Vehicle Collisions in Patients with Glaucoma: a Longitudinal Study. Plos one 10(10): E0138288

Karmakar, C.; Luo, W.; Tran, T.; Berk, M.; Venkatesh, S. 2016: Predicting Risk of Suicide Attempt Using History of Physical Illnesses from Electronic Medical Records. Jmir Mental Health 3(3): E19

Jelovsek, J.Eric.; Hill, A.Jolyn.; Chagin, K.M.; Kattan, M.W.; Barber, M.D. 2016: Predicting Risk of Urinary Incontinence and Adverse Events After Midurethral Sling Surgery in Women. Obstetrics and Gynecology 127(2): 330-340

Anderson, S.L.; Zheng, Y.; McMahon, R.J. 2017: Predicting Risky Sexual Behavior: the Unique and Interactive Roles of Childhood Conduct Disorder Symptoms and Callous-Unemotional Traits. Journal of Abnormal Child Psychology 45(6): 1147-1156

Askitas, N. 2016: Predicting Road Conditions with Internet Search. Plos one 11(8): E0162080

Shannon, C.K.; Price, S.S.; Jackson, J. 2016: Predicting Rural Practice and Service to Indigent Patients: Survey of Dental Students Before and After Rural Community Rotations. Journal of Dental Education 80(10): 1180-1187

Krauskopf, A.L.; Knippel, A.J.; Verde, P.E.; Kozlowski, P. 2016: Predicting SGA neonates using first-trimester screening: influence of previous pregnancy's birthweight and PAPP-A MoM. Journal of Maternal-Fetal and Neonatal Medicine: the Official Journal of the European Association of Perinatal Medicine the Federation of Asia and Oceania Perinatal Societies the International Society of Perinatal Obstetricians 29(18): 2962-2967

Pandey, P.; Cao, W.; Wang, Y.; Vaddella, V. 2016: Predicting Salmonella Typhimurium reductions in poultry ground carcasses. Poultry Science 95(11): 2640-2646

Lee, M.; Huang, Y.; Chong, H.C.; Ning, Y.; Lo, N.N.; Yeo, S.J. 2016: Predicting Satisfaction for Unicompartmental Knee Arthroplasty Patients in an Asian Population. Journal of Arthroplasty 31(8): 1706-1710

Kiware, S.S.; Corliss, G.; Merrill, S.; Lwetoijera, D.W.; Devine, G.; Majambere, S.; Killeen, G.F. 2015: Predicting Scenarios for Successful Autodissemination of Pyriproxyfen by Malaria Vectors from Their Resting Sites to Aquatic Habitats; Description and Simulation Analysis of a Field-Parameterizable Model. Plos one 10(7): E0131835

Shepperd, J.A.; Emanuel, A.S.; Howell, J.L.; Logan, H.L. 2015: Predicting Scheduling and Attending for an Oral Cancer Examination. Annals of Behavioral Medicine: a Publication of the Society of Behavioral Medicine 49(6): 828-838

Keefe, R.S.E.; Reichenberg, A. 2016: Predicting Schizophrenia. JAMA Psychiatry 73(5): 441-442

Hagger, M.S.; Hardcastle, S.J.; Hingley, C.; Strickland, E.; Pang, J.; Watts, G.F. 2016: Predicting Self-Management Behaviors in Familial Hypercholesterolemia Using an Integrated Theoretical Model: the Impact of Beliefs about Illnesses and Beliefs about Behaviors. International Journal of Behavioral Medicine 23(3): 282-294

Böhme, S.; Renneberg, B. 2015: Predicting Self-Rated Health in Diabetes and Chronic Heart Failure - a Multiple Mediation Model. Frontiers in Public Health 3: 266

Thornton, H.R.; Delaney, J.A.; Duthie, G.M.; Scott, B.R.; Chivers, W.J.; Sanctuary, C.E.; Dascombe, B.J. 2016: Predicting Self-Reported Illness for Professional Team-Sport Athletes. International Journal of Sports Physiology and Performance 11(4): 543-550

Schlegl, T.; Waldstein, S.M.; Vogl, W.-D.; Schmidt-Erfurth, U.; Langs, G. 2015: Predicting Semantic Descriptions from Medical Images with Convolutional Neural Networks. Information Processing in Medical Imaging: Proceedings of the . Conference 24: 437-448

Olenick, E.M.; Zimbro, K.S.; DʼLima, G.M.; Ver Schneider, P.; Jones, D. 2017: Predicting Sepsis Risk Using the "Sniffer" Algorithm in the Electronic Medical Record. Journal of Nursing Care Quality 32(1): 25-31

Puranik, S.; Forno, E.; Bush, A.; Celedón, J.C. 2017: Predicting Severe Asthma Exacerbations in Children. American Journal of Respiratory and Critical Care Medicine 195(7): 854-859

Williams, D.J.; Zhu, Y.; Grijalva, C.G.; Self, W.H.; Harrell, F.E.; Reed, C.; Stockmann, C.; Arnold, S.R.; Ampofo, K.K.; Anderson, E.J.; Bramley, A.M.; Wunderink, R.G.; McCullers, J.A.; Pavia, A.T.; Jain, S.; Edwards, K.M. 2016: Predicting Severe Pneumonia Outcomes in Children. Pediatrics 138(4)

Tanigasalam, V.; Bhat, B.V.; Adhisivam, B.; Sridhar, M.G.; Harichandrakumar, K.T. 2016: Predicting Severity of Acute Kidney Injury in Term Neonates with Perinatal Asphyxia Using Urinary Neutrophil Gelatinase Associated Lipocalin. Indian Journal of Pediatrics 83(12-13): 1374-1378

Casey, E.A.; Masters, N.T.; Beadnell, B.; Hoppe, M.J.; Morrison, D.M.; Wells, E.A. 2017: Predicting Sexual Assault Perpetration Among Heterosexually Active Young Men. Violence Against Women 23(1): 3-27

Relyea, M.; Ullman, S.E. 2017: Predicting Sexual Assault Revictimization in a Longitudinal Sample of Women Survivors: Variation by Type of Assault. Violence Against Women 23(12): 1462-1483

Diehl, C.; Rees, J.; Bohner, G. 2018: Predicting Sexual Harassment from Hostile Sexism and Short-Term Mating Orientation: Relative Strength of Predictors Depends on Situational Priming of Power Versus Sex. Violence Against Women 24(2): 123-143

Howes, A.; Duggan, G.B.; Kalidindi, K.; Tseng, Y-Chi.; Lewis, R.L. 2016: Predicting Short-Term Remembering as Boundedly Optimal Strategy Choice. Cognitive Science 40(5): 1192-1223

Basques, B.A.; Chung, S.H.; Lukasiewicz, A.M.; Webb, M.L.; Samuel, A.M.; Bohl, D.D.; Smith, B.G.; Grauer, J.N. 2015: Predicting Short-term Morbidity in Patients Undergoing Posterior Spinal Fusion for Neuromuscular Scoliosis. Spine 40(24): 1910-1917

Diec, J.; Tilia, D.; Naduvilath, T.; Bakaraju, R.C. 2017: Predicting Short-term Performance of Multifocal Contact Lenses. Eye and Contact Lens 43(6): 340-345

Fichtenholtz, A.M.; Camarda, N.D.; Neumann, E.K. 2016: Predicting Significance of Unknown Variants in Glial Tumors through Sub-Class Enrichment. Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing 21: 297-308

Jiao, Y.; Zhou, L.; Ma, F.; Gao, G.; Kou, L.; Bell, J.; Sanvito, S.; Du, A. 2016: Predicting Single-Layer Technetium Dichalcogenides (TcX₂, X = S, Se) with Promising Applications in Photovoltaics and Photocatalysis. Acs Applied Materials and Interfaces 8(8): 5385-5392

Asghar, K.A.; Rowlands, D.A.; Elliott, J.M.; Squires, A.M. 2015: Predicting Sizes of Hexagonal and Gyroid Metal Nanostructures from Liquid Crystal Templating. Acs Nano 9(11): 10970-10978

Shaw, H.; Ellis, D.A.; Kendrick, L.-R.; Ziegler, F.; Wiseman, R. 2016: Predicting Smartphone Operating System from Personality and Individual Differences. Cyberpsychology Behavior and Social Networking 19(12): 727-732

Hoogendoorn, M.; Berger, T.; Schulz, A.; Stolz, T.; Szolovits, P. 2017: Predicting Social Anxiety Treatment Outcome Based on Therapeutic Email Conversations. IEEE Journal of Biomedical and Health Informatics 21(5): 1449-1459

Liu, Y.; Pan, X.; Wang, C.; Li, Y.; Shi, R. 2015: Predicting Soil Salinity with Vis-NIR Spectra after Removing the Effects of Soil Moisture Using External Parameter Orthogonalization. Plos one 10(10): E0140688

Hydren, J.R.; Foulis, S.A.; Frykman, P.N.; Warr, B.J.; Redmond, J.E.; Canino, M.C.; Cohen, B.S.; Sharp, M.A.; Zambraski, E.J. 2016: Predicting Soldier Load Carriage Performance: 960 Board #276 June 1, 2: 00 PM - 3: 30 PM. Medicine and Science in Sports and Exercise 48(5 Suppl 1): 274-274

Redmond, J.E.; Foulis, S.A.; Frykman, P.N.; Warr, B.J.; Sharp, M.A.; Zambraski, E.J. 2016: Predicting Soldier Performance of the Casualty Evacuation: 943 Board #259 June 1, 2: 00 PM - 3: 30 PM. Medicine and Science in Sports and Exercise 48(5 Suppl 1): 268-269

Zhang, X.; Li, J.; Wei, D.; Li, B.; Ma, Y. 2015: Predicting Soluble Nickel in Soils Using Soil Properties and Total Nickel. Plos one 10(7): E0133920

Misin, M.; Palmer, D.S.; Fedorov, M.V. 2016: Predicting Solvation Free Energies Using Parameter-Free Solvent Models. Journal of Physical Chemistry. B 120(25): 5724-5731

Osburn, C.L.; Handsel, L.T.; Peierls, B.L.; Paerl, H.W. 2016: Predicting Sources of Dissolved Organic Nitrogen to an Estuary from an Agro-Urban Coastal Watershed. Environmental Science and Technology 50(16): 8473-8484

Bellamy, C.; Altringham, J. 2015: Predicting Species Distributions Using Record Centre Data: Multi-Scale Modelling of Habitat Suitability for Bat Roosts. Plos one 10(6): E0128440

Rong, P.; Yunusova, Y.; Wang, J.; Zinman, L.; Pattee, G.L.; Berry, J.D.; Perry, B.; Green, J.R. 2016: Predicting Speech Intelligibility Decline in Amyotrophic Lateral Sclerosis Based on the Deterioration of Individual Speech Subsystems. Plos one 11(5): E0154971

Ellis, R.J.; Molander, P.; Rönnberg, J.; Lyxell, B.ör.; Andersson, G.; Lunner, T. 2016: Predicting Speech-in-Noise Recognition from Performance on the Trail Making Test: Results from a Large-Scale Internet Study. Ear and Hearing 37(1): 73-79

Dangle, P.; Ayyash, O.; Shaikh, H.; Stephany, H.A.; Cannon, G.M.; Schneck, F.X.; Ost, M.C. 2016: Predicting Spontaneous Stone Passage in Prepubertal Children: A Single Institution Cohort. Journal of Endourology 30(9): 945-949

Carpentier, Jëlle.; Mageau, Gève.A. 2016: Predicting Sport Experience During Training: The Role of Change-Oriented Feedback in Athletes' Motivation, Self-Confidence and Needs Satisfaction Fluctuations. Journal of Sport and Exercise Psychology 38(1): 45-58

Miller, N.A.; Kirk, A. 2016: Predicting State Investment in Medicaid Home- and Community-Based Services, 2000-2011. Journal of Aging and Social Policy 28(1): 49-64

Rasool, M.Fawad.; Khalil, F.; Läer, S. 2016: Predicting Stereoselective Disposition of Carvedilol in Adult and Pediatric Chronic Heart Failure Patients by Incorporating Pathophysiological Changes in Organ Blood Flows-A Physiologically Based Pharmacokinetic Approach. Drug Metabolism and Disposition: the Biological Fate of Chemicals 44(7): 1103-1115

Manuel, D.G.; Tuna, M.; Perez, R.; Tanuseputro, P.; Hennessy, D.; Bennett, C.; Rosella, L.; Sanmartin, C.; van Walraven, C.; Tu, J.V. 2015: Predicting Stroke Risk Based on Health Behaviours: Development of the Stroke Population Risk Tool (SPoRT). Plos one 10(12): E0143342

Safder, T.B.; Badgett, R.G. 2016: Predicting Stroke in Patients with Atrial Fibrillation: An Incomplete Picture Without Considering Quality of Anticoagulation. Journal of the American College of Cardiology 67(18): 2192-2193

Fredin, L.A.; Allison, T.C. 2016: Predicting Structures of Ru-Centered Dyes: a Computational Screening Tool. Journal of Physical Chemistry. a 120(13): 2135-2143

Wang, X.; Li, H.; Zhang, Q.; Wang, R. 2016: Predicting Subcellular Localization of Apoptosis Proteins Combining GO Features of Homologous Proteins and Distance Weighted KNN Classifier. Biomed Research International 2016: 1793272

Nielsen, H. 2017: Predicting Subcellular Localization of Proteins by Bioinformatic Algorithms. Current Topics in Microbiology and Immunology 404: 129-158

Schmack, K.; Burk, J.; Haynes, J.-D.; Sterzer, P. 2016: Predicting Subjective Affective Salience from Cortical Responses to Invisible Object Stimuli. Cerebral Cortex 26(8): 3453-3460

Valeri, L.; Patterson-Lomba, O.; Gurmu, Y.; Ablorh, A.; Bobb, J.; Townes, F.William.; Harling, G. 2016: Predicting Subnational Ebola Virus Disease Epidemic Dynamics from Sociodemographic Indicators. Plos one 11(10): E0163544

Sahebally, S.M. 2017: Predicting Suboptimal Bowel Preparation: Taking it up a PEG. Digestive Diseases and Sciences 62(2): 289-291

Lester, D. 2016: Predicting Success in Psychological Statistics Courses. Psychological Reports 118(3): 772-777

Hu, D. 2016: Predicting Success in Residency: the Quarterback Problem. Hospital Pharmacy 51(8): 615-617

Hollemans, R.A.; Bollen, T.L.; van Brunschot, S.; Bakker, O.J.; Ahmed Ali, U.; van Goor, H.; Boermeester, M.A.; Gooszen, H.G.; Besselink, M.G.; van Santvoort, H.C. 2016: Predicting Success of Catheter Drainage in Infected Necrotizing Pancreatitis. Annals of Surgery 263(4): 787-792

Al Fayyadh, M.J.; Heller, S.F.; Rajab, T.Konrad.; Gardner, A.K.; Bloom, J.P.; Rawlings, J.A.; Mullen, J.T.; Smink, D.S.; Farley, D.R.; Willis, R.E.; Dent, D.L. 2016: Predicting Success of Preliminary Surgical Residents: A Multi-Institutional Study. Journal of surgical education 73(6): e77-e83

Kawabata, K.; Watanabe, H.; Hara, K.; Bagarinao, E.; Yoneyama, N.; Ogura, A.; Imai, K.; Masuda, M.; Yokoi, T.; Ohdake, R.; Tanaka, Y.; Tsuboi, T.; Nakamura, T.; Hirayama, M.; Ito, M.; Atsuta, N.; Maesawa, S.; Naganawa, S.; Katsuno, M.; Sobue, G. 2018: Distinct manifestation of cognitive deficits associate with different resting-state network disruptions in non-demented patients with Parkinson's disease. Journal of Neurology 265(3): 688-700

Araújo, L.; Ribeiro, O.; Teixeira, L.; Paúl, Cça. 2016: Predicting Successful Aging at One Hundred Years of Age. Research on aging 38(6): 689-709

Barak-Corren, Y.; Castro, V.M.; Javitt, S.; Hoffnagle, A.G.; Dai, Y.; Perlis, R.H.; Nock, M.K.; Smoller, J.W.; Reis, B.Y. 2017: Predicting Suicidal Behavior From Longitudinal Electronic Health Records. American Journal of Psychiatry 174(2): 154-162

Sage, C.C.; Platko, J.V.; Nokela, M. 2014: Predicting Suicidal Behavior In Veterans And Active Military Personnel: Possibilities For Electronic Deployment To Discover A Predictive Assessment. Value in Health: the Journal of the International Society for Pharmacoeconomics and Outcomes Research 17(7): A569-A570

Jordan, J.T.; Samuelson, K.W. 2016: Predicting Suicide Intent: the Roles of Experiencing or Committing Violent Acts. Suicide and Life-Threatening Behavior 46(3): 293-300

Sarma, K.; Kumar, A.; Krishna, M.; Medhi, M.; Tripathi, O.P. 2015: Predicting Suitable Habitats for the Vulnerable Eastern Hoolock Gibbon, Hoolock leuconedys, in India Using the MaxEnt Model. Folia Primatologica; International Journal of Primatology 86(4): 387-397

Hutchings, F.; Han, C.E.; Keller, S.S.; Weber, B.; Taylor, P.N.; Kaiser, M. 2015: Predicting Surgery Targets in Temporal Lobe Epilepsy through Structural Connectome Based Simulations. Plos Computational Biology 11(12): E1004642

Barrera, J.é E.; Dion, G.R. 2016: Predicting Surgical Response Using Tensiometry in OSA Patients after Genioglossus Advancement with Uvulopalatopharyngoplasty. Otolaryngology--Head and Neck Surgery: Official Journal of American Academy of Otolaryngology-Head and Neck Surgery 154(3): 558-563

Rades, D.; Dziggel, L.; Hakim, S.G.; Rudat, V.; Janssen, S.; Trang, N.T.; Khoa, M.T.; Bartscht, T. 2015: Predicting Survival After Irradiation for Brain Metastases from Head and Neck Cancer. In Vivo 29(5): 525-528

Bolm, L.; Janssen, S.; Käsmann, L.; Wellner, U.; Bartscht, T.; Schild, S.E.; Rades, D. 2015: Predicting Survival After Irradiation of Metastases from Pancreatic Cancer. Anticancer Research 35(7): 4105-4108

Manig, L.; Käsmann, L.; Janssen, S.; Rades, D. 2016: Predicting Survival After Irradiation of Metastases from Transitional Carcinoma of the Bladder. Anticancer Research 36(12): 6663-6665

Glei, D.A.; Goldman, N.; Risques, R.Ana.; Rehkopf, D.H.; Dow, W.H.; Rosero-Bixby, L.; Weinstein, M. 2016: Predicting Survival from Telomere Length versus Conventional Predictors: A Multinational Population-Based Cohort Study. Plos one 11(4): E0152486

Karnik, N.D.; Gupta, A.V. 2016: Predicting Survival in ARDS. Journal of the Association of Physicians of India 63(11): 11-13

Graff-Radford, J.; Lesnick, T.G.; Boeve, B.F.; Przybelski, S.A.; Jones, D.T.; Senjem, M.L.; Gunter, J.L.; Ferman, T.J.; Knopman, D.S.; Murray, M.E.; Dickson, D.W.; Sarro, L.; Jack, C.R.; Petersen, R.C.; Kantarci, K. 2016: Predicting Survival in Dementia with Lewy Bodies with Hippocampal Volumetry. Movement Disorders: Official Journal of the Movement Disorder Society 31(7): 989-994

Albaeni, A.; Eid, S.M.; Vaidya, D.; Chandra-Strobos, N. 2014: Predicting Survival with Good Neurological Outcome Within 24 Hours Following Out of Hospital Cardiac Arrest:The Application and Validation of a Novel Clinical Score. Journal of Neurology and Translational Neuroscience 2(1)

Edwards, A.; Eisenberg, N.; Spinrad, T.L.; Reiser, M.; Eggum-Wilkens, N.D.; Liew, J. 2015: Predicting Sympathy and Prosocial Behavior from Young Children's Dispositional Sadness. Social Development 24(1): 76-94

Novak, D.; Riener, R. 2015: Predicting Targets of Human Reaching Motions with an Arm Rehabilitation Exoskeleton. Biomedical Sciences Instrumentation 51: 385-392

Hagen, G. 2015: Predicting The Future Economic Burden of Hip Fractures In Norway-The Impact of Epidemiological Uncertainty. Value in Health 18(7): A642

Grogan, J.A.; Markelc, B.; Connor, A.J.; Muschel, R.J.; Pitt-Francis, J.M.; Maini, P.K.; Byrne, H.M. 2017: Predicting the Influence of Microvascular Structure on Tumor Response to Radiotherapy. IEEE Transactions on Bio-Medical Engineering 64(3): 504-511

Young, A.S.; Meers, M.R.; Vesco, A.T.; Seidenfeld, A.M.; Arnold, L.Eugene.; Fristad, M.A. 2019: Predicting Therapeutic Effects of Psychodiagnostic Assessment Among Children and Adolescents Participating in Randomized Controlled Trials. Journal of Clinical Child and Adolescent Psychology: the Official Journal for the Society of Clinical Child and Adolescent Psychology American Psychological Association Division 53 48(Sup1): S1-S12

Atreya, R.; Neurath, M.F. 2016: Predicting Therapeutic Response by in vivo Molecular Imaging in Inflammatory Bowel Diseases. Digestive Diseases 34(5): 552-557

Kitadai, N. 2016: Predicting Thermodynamic Behaviors of Non-Protein Amino Acids as a Function of Temperature and pH. Origins of Life and Evolution of the Biosphere: the Journal of the International Society for the Study of the Origin of Life 46(1): 3-18

Xiao, F.; Peng, G.; Nie, C.; Yu, L. 2016: Predicting Thermodynamic Properties of PBXTHs with new Quantum Topological Indexes. Plos one 11(2): E0147126

Moreira, D.M.; Howard, L.E.; Sourbeer, K.N.; Amarasekara, H.S.; Chow, L.C.; Cockrell, D.C.; Pratson, C.L.; Hanyok, B.T.; Aronson, W.J.; Kane, C.J.; Terris, M.K.; Amling, C.L.; Cooperberg, M.R.; Freedland, S.J. 2017: Predicting time from Metastasis to Overall Survival in Castration-Resistant Prostate Cancer: Results from SEARCH. Clinical Genitourinary Cancer 15(1): 60-66.E2

Cooke, M.E.; Nasim, A.; Cho, S.Bin.; Kendler, K.S.; Clark, S.L.; Dick, D.M. 2016: Predicting Tobacco Use across the First Year of College. American journal of health behavior 40(4): 484-495

Basant, N.; Gupta, S.; Singh, K.P. 2015: Predicting Toxicities of Diverse Chemical Pesticides in Multiple Avian Species Using Tree-Based QSAR Approaches for Regulatory Purposes. Journal of Chemical Information and Modeling 55(7): 1337-1348

Klintström, E.; Klintström, B.; Moreno, R.; Brismar, T.B.; Pahr, D.H.; Smedby, Ör. 2016: Predicting Trabecular Bone Stiffness from Clinical Cone-Beam CT and HR-pQCT Data; an in Vitro Study Using Finite Element Analysis. Plos one 11(8): E0161101

Tauber, H.; Streif, W.; Fritz, J.; Ott, H.; Weigel, G.; Loacker, L.; Heinz, A.; Velik-Salchner, C. 2016: Predicting Transfusion Requirements During Extracorporeal Membrane Oxygenation. Journal of Cardiothoracic and Vascular Anesthesia 30(3): 692-701

Ruocco, A.C.; Rodrigo, A.H.; McMain, S.F.; Page-Gould, E.; Ayaz, H.; Links, P.S. 2016: Predicting Treatment Outcomes from Prefrontal Cortex Activation for Self-Harming Patients with Borderline Personality Disorder: a Preliminary Study. Frontiers in Human Neuroscience 10: 220

Andreescu, C.; Aizenstein, H. 2016: Predicting Treatment Response with Functional Magnetic Resonance Imaging. Biological Psychiatry 79(4): 262-263

Shenk, C.E.; Dorn, L.D.; Kolko, D.J.; Susman, E.J.; Noll, J.G.; Bukstein, O.G. 2012: Predicting Treatment Response for Oppositional Defiant and Conduct Disorder Using Pre-treatment Adrenal and Gonadal Hormones. Journal of Child and Family Studies 21(6): 973-981

Mattek, R.J.; Harris, S.E.; Fox, R.A. 2016: Predicting Treatment Success in Child and Parent Therapy Among Families in Poverty. Journal of Genetic Psychology 177(2): 44-54

Guitar, B.; Kazenski, D.; Howard, A.; Cousins, S.Freddie.; Fader, E.; Haskell, P. 2015: Predicting Treatment Time and Long-Term Outcome of the Lidcombe Program: A Replication and Reanalysis. American Journal of Speech-Language Pathology 24(3): 533-544

Tsai, M.-F.; Hwang, S.-L.; Tsay, S.-L.; Wang, C.-L.; Tsai, F.-C.; Chen, C.-C.; Huang, T.-Y. 2013: Predicting Trends in Dyspnea and Fatigue in Heart Failure Patients' Outcomes. Acta Cardiologica Sinica 29(6): 488-495

Harper, L.K.; Shoaf, A.L.; Bayse, C.A. 2015: Predicting Trigger Bonds in Explosive Materials through Wiberg Bond Index Analysis. Chemphyschem: a European Journal of Chemical Physics and Physical Chemistry 16(18): 3886-3892

Shi, Z.; Zhang, Z.; Kutana, A.; Yakobson, B.I. 2015: Predicting Two-Dimensional Silicon Carbide Monolayers. Acs Nano 9(10): 9802-9809

Vaala, S.E.; Hornik, R.C. 2014: Predicting US Infants' and Toddlers' TV/Video Viewing Rates: Mothers' Cognitions and Structural Life Circumstances. Journal of Children and Media 8(2): 163-182

Khanna, S.; Good, N.; Boyle, J. 2016: Predicting Unpanned Return to Hospital for Chronic Disease Patients. Studies in Health Technology and Informatics 227: 67-73

Rosenbaum, J.E.; Zenilman, J.; Rose, E.; Wingood, G.; DiClemente, R. 2016: Predicting Unprotected Sex and Unplanned Pregnancy among Urban African-American Adolescent Girls Using the Theory of Gender and Power. Journal of urban health: bulletin of the New York Academy of Medicine 93(3): 493-510

Baranowski, T.; Beltran, A.; Chen, T.-A.; Thompson, D.; O'Connor, T.; Hughes, S.; Diep, C.; Baranowski, J.C. 2013: Predicting use of Ineffective Responsive, Structure and Control Vegetable Parenting Practices with the Model of Goal Directed Behavior. Journal of Food Research 2(6): 80-88

Loe, H.; Nes, B.M.; Wisløff, U. 2016: Predicting VO2peak from Submaximal- and Peak Exercise Models: the HUNT 3 Fitness Study, Norway. Plos one 11(1): E0144873

Chen, N-Tsu.Nancy. 2015: Predicting Vaccination Intention and Benefit and Risk Perceptions: The Incorporation of Affect, Trust, and Television Influence in a Dual-Mode Model. Risk Analysis: An Official Publication of the Society for Risk Analysis 35(7): 1268-1280

Hohtari-Kivimäki, U.; Salminen, M.; Vahlberg, T.; Kivelä, S-Liisa. 2016: Predicting Value of Nine-Item Berg Balance Scale Among the Aged: A 3-Year Prospective Follow-Up Study. Experimental Aging Research 42(2): 151-160

Grieb, M.; Burkovski, A.; Sträng, J.E.; Kraus, J.M.; Groß, A.; Palm, G.ün.; Kühl, M.; Kestler, H.A. 2015: Predicting Variabilities in Cardiac Gene Expression with a Boolean Network Incorporating Uncertainty. Plos one 10(7): E0131832

Kalakoti, P.; Notarianni, C.; Nanda, A. 2016: Predicting Venous Thromboembolism in Pediatric Trauma Patients. JAMA Surgery 151(9): 881-882

Karaca, O.; Gunes, H.M.; Omaygenc, M.O.; Cakal, B.; Cakal, S.D.; Demir, G.G.; Kizilirmak, F.; Gokdeniz, T.; Barutcu, I.; Boztosun, B.; Kilicaslan, F. 2016: Predicting Ventricular Arrhythmias in Cardiac Resynchronization Therapy: the Impact of Persistent Electrical Dyssynchrony. Pacing and Clinical Electrophysiology: Pace 39(9): 969-977

Kim, Y.B.; Park, N.; Zhang, Q.; Kim, J.G.; Kang, S.J.; Kim, C.H. 2016: Predicting Virtual World User Population Fluctuations with Deep Learning. Plos one 11(12): E0167153

Skrivanova, K.; Anderkova, L.; Brancikova, D.; Jarkovský, J.; Benesova, K.; Elfmarková, N.; Svěrák, T.; Bendová, M.; Peterkova, H.; Nedvěd, J.; Protivánková, M.; Minar, L.; Holoubková, E.; Dusek, L. 2016: Predicting Vitality Change in Older Breast Cancer Survivors after Primary Treatment--an Approach Based on Using Time-related Difference of Pro-inflammatory Marker C-reactive Protein. Klinicka Onkologie: Casopis Ceske a Slovenske Onkologicke Spolecnosti 29(1): 52-58

Guo, H.; Wang, Z.; Zhang, Y. 2016: Predicting Vo2max From A 6-minute Stairs Climbing And Descending Test (6MSCDT): 355 Board #192 June 1, 11: 00 AM - 12: 30 PM. Medicine and Science in Sports and Exercise 48(5 Suppl 1): 93-93

Paulo, A.; Zaal, F.T.J.M.; Fonseca, S.; Araújo, D. 2016: Predicting Volleyball Serve-Reception. Frontiers in Psychology 7: 1694

Cook, P.G.; Miller, A.; Shanafield, M.; Simmons, C.T. 2017: Predicting Water Resource Impacts of Unconventional Gas Using Simple Analytical Equations. Ground Water 55(3): 387-398

Lau, S.L.; Muir, C.; Assur, Y.; Beach, R.; Tran, B.; Bartrop, R.; McLean, M.; Caetano, D. 2016: Predicting Weight Gain in Patients Treated with Clozapine: the Role of Sex, Body Mass Index, and Smoking. Journal of Clinical Psychopharmacology 36(2): 120-124

Shand, L.; Brown, W.M.; Chaves, L.F.; Goldberg, T.L.; Hamer, G.L.; Haramis, L.; Kitron, U.; Walker, E.D.; Ruiz, M.O. 2016: Predicting West Nile Virus Infection Risk from the Synergistic Effects of Rainfall and Temperature. Journal of Medical Entomology 53(4): 935-944

Warren, B.H.; Baudin, R.ém.; Franck, A.; Hugel, S.; Strasberg, D. 2016: Predicting Where a Radiation will Occur: Acoustic and Molecular Surveys Reveal Overlooked Diversity in Indian Ocean Island Crickets (Mogoplistinae: Ornebius). Plos one 11(2): E0148971

Hartjes, T.M. 2015: Predicting which Patients will Benefit from Palliative Care: use of Bundles, Triggers, and Protocols. Critical Care Nursing Clinics of North America 27(3): 307-314

Mareiniss, D.P.; Xu, T.; Pham, J.Cuong.; Hsieh, Y-Hsiang.; Zhao, J.; Nguyen, C.; Nguyen, M.; Winters, B. 2016: Predicting Which Patients will Likely Benefit from Subglottic Secretion Drainage Endotracheal Tubes: A Retrospective Study. Journal of Emergency Medicine 50(3): 385-393

Hancock, P.A.; White, V.L.; Ritchie, S.A.; Hoffmann, A.A.; Godfray, H.C.J. 2016: Predicting Wolbachia invasion dynamics in Aedes aegypti populations using models of density-dependent demographic traits. Bmc Biology 14(1): 96

Lambrechts, L. 2015: Predicting Wolbachia potential to knock down dengue virus transmission. Annals of Translational Medicine 3(19): 288

Mendoza, K.; Ulloa, A.; Saavedra, N.; Galván, J.; Berenzon, S. 2017: Predicting Women's Utilization of Primary Care Mental Health Services in Mexico City. Journal of Primary Care and Community Health 8(2): 83-88

Van Benschoten, A.H.; Afonine, P.V.; Terwilliger, T.C.; Wall, M.E.; Jackson, C.J.; Sauter, N.K.; Adams, P.D.; Urzhumtsev, A.; Fraser, J.S. 2015: Predicting X-ray diffuse scattering from translation-libration-screw structural ensembles. Acta Crystallographica. Section D Biological Crystallography 71(Pt 8): 1657-1667

Maggs, J.L.; Staff, J.; Kloska, D.D.; Patrick, M.E.; O'Malley, P.M.; Schulenberg, J. 2015: Predicting Young Adult Degree Attainment by Late Adolescent Marijuana Use. Journal of adolescent health: official publication of the Society for Adolescent Medicine 57(2): 205-211

Papp, L.M.; Kouros, C.D. 2017: Predicting young adults' risk for engaging in prescription drug misuse in daily life from individual, partner, and relationship factors. Substance Abuse 38(1): 61-68

Sichimba, F.; Mooya, H.; Mesman, J. 2017: Predicting Zambian Grandmothers' Sensitivity Toward their Grandchildren. International Journal of Aging and Human Development 85(2): 185-203

Cox, B.D.; Stanton, R.A.; Schinazi, R.F. 2015: Predicting Zika virus structural biology: Challenges and opportunities for intervention. Antiviral Chemistry and ChemoTherapy 24(3-4): 118-126

Guan, C.; Niu, X.; Shi, F.; Yang, K.; Li, N. 2015: Predicting a DNA-binding protein using random forest with multiple mathematical features. Bio-Medical Materials and Engineering 26(Suppl 1): S1883-S1889

Kasir, R.; Zakko, S.; Zakko, P.; Adler, M.; Lee, A.; Dhingra, S.; Guttermuth, C. 2016: Predicting a Response to Antibiotics in Patients with the Irritable Bowel Syndrome. Digestive Diseases and Sciences 61(3): 846-851

Roberts, A.M.I.; Tansey, C.; Smithers, R.J.; Phillimore, A.B. 2015: Predicting a change in the order of spring phenology in temperate forests. Global Change Biology 21(7): 2603-2611

Petersen, T.; Christensen, R.; Juhl, C. 2015: Predicting a clinically important outcome in patients with low back pain following McKenzie therapy or spinal manipulation: a stratified analysis in a randomized controlled trial. Bmc Musculoskeletal Disorders 16: 74

Scior, T.; Paiz-Candia, B.; Islas, Án.A.; Sánchez-Solano, A.; Millan-Perez Peña, L.; Mancilla-Simbro, C.; Salinas-Stefanon, E.M. 2015: Predicting a double mutant in the twilight zone of low homology modeling for the skeletal muscle voltage-gated sodium channel subunit beta-1 (Nav1.4 β1). Computational and Structural Biotechnology Journal 13: 229-240

Wang, P.S.; Ren, W.; Bellaiche, L.; Xiang, H.J. 2015: Predicting a ferrimagnetic phase of Zn(2)FeOsO(6) with strong magnetoelectric coupling. Physical Review Letters 114(14): 147204

Schwab, S.; Gebhardt, M.; Hessels, M.G.P.; Nusser, L. 2016: Predicting a high rate of self-assessed and parent-assessed peer problems--Is it typical for students with disabilities?. Research in Developmental Disabilities 49-50: 196-204

Ma, F.; Gao, G.; Jiao, Y.; Gu, Y.; Bilic, A.; Zhang, H.; Chen, Z.; Du, A. 2016: Predicting a new phase (T'') of two-dimensional transition metal di-chalcogenides and strain-controlled topological phase transition. Nanoscale 8(9): 4969-4975

Huerta, S. 2016: Predicting a pathological complete response in rectal cancer. Anti-Cancer Drugs 27(8): 709-710

Shamid, S.M.; Allender, D.W.; Selinger, J.V. 2014: Predicting a polar analog of chiral blue phases in liquid crystals. Physical Review Letters 113(23): 237801

Nipp, R.D.; Ryan, D.P. 2015: Predicting a response to FOLFIRINOX in pancreatic cancer. Journal of the National Cancer Institute 107(8)

Ljungquist, K.L.; Agnew, S.P.; Huang, J.I. 2015: Predicting a safe screw length for volar plate fixation of distal radius fractures: lunate depth as a marker for distal radius depth. Journal of Hand Surgery 40(5): 940-944

Lee, C.-H.; Lee, C.-C.; Hsieh, C.-C.; Hong, M.-Y.; Chi, C.-H. 2016: Predicting abscesses in adults with community-onset monomicrobial Enterobacteriaceae bacteremia: microorganisms matters. Diagnostic Microbiology and Infectious Disease 84(1): 74-79

Schouteten, R. 2017: Predicting absenteeism: screening for work ability or burnout. Occupational Medicine 67(1): 52-57

Adegbija, O.; Hoy, W.; Wang, Z. 2015: Predicting absolute risk of type 2 diabetes using age and waist circumference values in an aboriginal Australian community. Plos one 10(4): E0123788

Fort, H.; Mungan, M. 2015: Predicting abundances of plants and pollinators using a simple compartmental mutualistic model. PROCEEDINGS. Biological Sciences 282(1808): 20150592

Yost, M.J.; Gardner, J.; Bell, R.M.; Fann, S.A.; Lisk, J.R.; Cheadle, W.G.; Goldman, M.H.; Rawn, S.; Weigelt, J.A.; Termuhlen, P.M.; Woods, R.J.; Endean, E.D.; Kimbrough, J.; Hulme, M. 2015: Predicting academic performance in surgical training. Journal of Surgical Education 72(3): 491-499

Al-Ansari, A.A.; El Tantawi, M.M.A. 2015: Predicting academic performance of dental students using perception of educational environment. Journal of Dental Education 79(3): 337-344

Jensen, J.H. 2015: Predicting accurate absolute binding energies in aqueous solution: thermodynamic considerations for electronic structure methods. Physical Chemistry Chemical Physics: Pccp 17(19): 12441-12451

Levin, M.; Kaforou, M. 2016: Predicting active tuberculosis progression by RNA analysis. Lancet 387(10035): 2268-2270

Abu El-Atta, A.H.; Moussa, M.I.; Hassanien, A.E. 2015: Predicting activity approach based on new atoms similarity kernel function. Journal of Molecular Graphics and Modelling 60: 55-62

Como, F.; Carnesecchi, E.; Volani, S.; Dorne, J.L.; Richardson, J.; Bassan, A.; Pavan, M.; Benfenati, E. 2017: Predicting acute contact toxicity of pesticides in honeybees (Apis mellifera) through a k-nearest neighbor model. Chemosphere 166: 438-444

Honore, P.M.; Jacobs, R.; Hendrickx, I.; De Waele, E.; Van Gorp, V.; Spapen, H.D. 2015: Predicting acute kidney injury in severe trauma. a biomarker breakthrough?. Critical Care 19: 432

Sosnov, J.A.; Stewart, I.J.; Chung, K.K. 2015: Predicting acute kidney injury in severe trauma. A biomarker breakthrough? Authors' response. Critical Care 19: 432

Szerlip, H.M.; Chawla, L.S. 2016: Predicting acute kidney injury prognosis. Current Opinion in Nephrology and Hypertension 25(3): 226-231

Kumarasena, R.S.; Niriella, M.A.; Ranawaka, C.K.; Miththinda, J.K.N.D.; de Silva, A.P.; Dassanayaka, A.S.; de Silva, H.J. 2016: Predicting acute liver failure in dengue infection. Ceylon Medical Journal 61(1): 35-36

West, J.B. 2014: Predicting acute mountain sickness. High Altitude Medicine and Biology 15(4): 427

Sajadi, S.Fatemeh.; Hajjari, Z.; Zargar, Y.; Mehrabizade Honarmand, M.; Arshadi, N. 2014: Predicting addiction potential on the basis of early traumatic events, dissociative experiences, and suicide ideation. International Journal of High Risk Behaviors and Addiction 3(4): E20995

Ramsay, D.S.; Al-Noori, S.; Shao, J.; Leroux, B.G.; Woods, S.C.; Kaiyala, K.J. 2015: Predicting addictive vulnerability: individual differences in initial responding to a drug's pharmacological effects. Plos one 10(4): E0124740

Depeursinge, A.; Yanagawa, M.; Leung, A.N.; Rubin, D.L. 2015: Predicting adenocarcinoma recurrence using computational texture models of nodule components in lung CT. Medical Physics 42(4): 2054-2063

Paoli, J. 2015: Predicting adequate surgical margins for cutaneous squamous cell carcinoma with dermoscopy. British Journal of Dermatology 172(5): 1186-1187

Karanasiou, G.S.; Tripoliti, E.E.; Papadopoulos, T.G.; Kalatzis, F.G.; Goletsis, Y.; Naka, K.K.; Bechlioulis, A.; Errachid, A.; Fotiadis, D.I. 2016: Predicting adherence of patients with HF through machine learning techniques. Healthcare Technology Letters 3(3): 165-170

Vitalis, D. 2017: Predicting adherence to antiretroviral therapy among pregnant women in Guyana: Utility of the Health Belief Model. International Journal of Std and Aids 28(8): 756-765

Holmes, E.A.F.; Hughes, D.A.; Morrison, V.L. 2014: Predicting adherence to medications using health psychology theories: a systematic review of 20 years of empirical research. Value in health: the journal of the International Society for Pharmacoeconomics and Outcomes Research 17(8): 863-876

Evangelista, L.S.; Ghasemzadeh, H.; Lee, J.-A.; Fallahzadeh, R.; Sarrafzadeh, M.; Moser, D.K. 2017: Predicting adherence to use of remote health monitoring systems in a cohort of patients with chronic heart failure. Technology and Health Care: Official Journal of the European Society for Engineering and Medicine 25(3): 425-433

Franklin, J.M.; Krumme, A.A.; Shrank, W.H.; Matlin, O.S.; Brennan, T.A.; Choudhry, N.K. 2015: Predicting adherence trajectory using initial patterns of medication filling. American Journal of Managed Care 21(9): E537-E544

Cameron, A.; Ireland, A.J.; McKay, G.A.; Stark, A.; Lowe, D.J. 2017: Predicting admission at triage: are nurses better than a simple objective score?. Emergency Medicine Journal: Emj 34(1): 2-7

Luben, R.; Hayat, S.; Wareham, N.; Khaw, K.T. 2016: Predicting admissions and time spent in hospital over a decade in a population-based record linkage study: the EPIC-Norfolk cohort. Bmj Open 6(1): E009461

Hipwell, A.E.; Stepp, S.D.; Moses-Kolko, E.L.; Xiong, S.; Paul, E.; Merrick, N.; McClelland, S.; Verble, D.; Keenan, K. 2016: Predicting adolescent postpartum caregiving from trajectories of depression and anxiety prior to childbirth: a 5-year prospective study. Archives of Women's Mental Health 19(5): 871-882

Yu, C.; Li, X.; Zhang, W. 2015: Predicting adolescent problematic online game use from teacher autonomy support, basic psychological needs satisfaction, and school engagement: a 2-year longitudinal study. Cyberpsychology Behavior and Social Networking 18(4): 228-233

Barlett, C.P. 2015: Predicting adolescent's cyberbullying behavior: a longitudinal risk analysis. Journal of Adolescence 41: 86-95

Kertai, M.D.; Fontes, M.L. 2015: Predicting adrenal insufficiency in severe sepsis: the role of plasma-free cortisol. Critical Care Medicine 43(3): 715-716

Simmonds, M.; Llewellyn, A.; Owen, C.G.; Woolacott, N. 2016: Predicting adult obesity from childhood obesity: a systematic review and meta-analysis. Obesity Reviews: An Official Journal of the International Association for the Study of Obesity 17(2): 95-107

Ladabaum, U.; Patel, A.; Mannalithara, A.; Sundaram, V.; Mitani, A.; Desai, M. 2016: Predicting advanced neoplasia at colonoscopy in a diverse population with the National Cancer Institute colorectal cancer risk-assessment tool. Cancer 122(17): 2663-2670

de Gelder, J.; Lucke, J.A.; de Groot, B.; Fogteloo, A.J.; Anten, S.; Mesri, K.; Steyerberg, E.W.; Heringhaus, C.; Blauw, G.J.; Mooijaart, S.P. 2016: Predicting adverse health outcomes in older emergency department patients: the APOP study. Netherlands Journal of Medicine 74(8): 342-352

Van Hazebroek, B.C.M.; Olthof, T.; Goossens, F.A. 2017: Predicting aggression in adolescence: the interrelation between (a lack of) empathy and social goals. Aggressive Behavior 43(2): 204-214

Caiozzo, C.N.; Houston, J.; Grych, J. 2016: Predicting aggression in late adolescent romantic relationships: a short-term longitudinal study. Journal of Adolescence 53: 237-248

Banse, R.; Messer, M.; Fischer, I. 2015: Predicting aggressive behavior with the aggressiveness-IAT. Aggressive Behavior 41(1): 65-83

Furuhama, A.; Hasunuma, K.; Hayashi, T.I.; Tatarazako, N. 2016: Predicting algal growth inhibition toxicity: three-step strategy using structural and physicochemical properties. Sar and Qsar in Environmental Research 27(5): 343-362

LaMartina, J.; Jawa, A.; Stucken, C.; Merlin, G.; Tornetta, P. 2015: Predicting alignment after closed reduction and casting of distal radius fractures. Journal of Hand Surgery 40(5): 934-939

Zhang, W.B.; Pincus, Z. 2016: Predicting all-cause mortality from basic physiology in the Framingham Heart Study. Aging Cell 15(1): 39-48

Nguyen, O.K.; Makam, A.N.; Clark, C.; Zhang, S.; Xie, B.; Velasco, F.; Amarasingham, R.; Halm, E.A. 2016: Predicting all-cause readmissions using electronic health record data from the entire hospitalization: Model development and comparison. Journal of Hospital Medicine 11(7): 473-480

Wright, D.F.B.; Duffull, S.B.; Merriman, T.R.; Dalbeth, N.; Barclay, M.L.; Stamp, L.K. 2016: Predicting allopurinol response in patients with gout. British Journal of Clinical Pharmacology 81(2): 277-289

Sha, S.J.; Ghosh, P.M.; Lee, S.E.; Corbetta-Rastelli, C.; Jagust, W.J.; Kornak, J.; Rankin, K.P.; Grinberg, L.T.; Vinters, H.V.; Mendez, M.F.; Dickson, D.W.; Seeley, W.W.; Gorno-Tempini, M.; Kramer, J.; Miller, B.L.; Boxer, A.L.; Rabinovici, G.D. 2015: Predicting amyloid status in corticobasal syndrome using modified clinical criteria, magnetic resonance imaging and fluorodeoxyglucose positron emission tomography. Alzheimer's Research and Therapy 7(1): 8

Wang, J.; Ke, C.; Yu, Z.; Fu, L.; Dornseif, B. 2016: Predicting analysis time in events-driven clinical trials using accumulating time-to-event surrogate information. Pharmaceutical Statistics 15(3): 198-207

Chen, T.-T. 2016: Predicting analysis times in randomized clinical trials with cancer immunotherapy. Bmc Medical Research Methodology 16: 12

Phan, C.-B.; Koo, S. 2015: Predicting anatomical landmarks and bone morphology of the femur using local region matching. International Journal of Computer Assisted Radiology and Surgery 10(11): 1711-1719

Shpak, A.A.; Shkvorchenko, D.O.; Sharafetdinov, I.K.; Yukhanova, O.A. 2016: Predicting anatomical results of surgical treatment of idiopathic macular hole. International Journal of Ophthalmology 9(2): 253-257

Sabir, S.; Ahrar, K.; Matin, S.F. 2016: Predicting and Determining the Success of Percutaneous Ablation. Journal of Urology 196(1): 7-8

Zhu, L.; Zhu, L.; Wang, H.; Yan, J.; Liu, B.; Chen, W.; He, J.; Zhou, Z.; Yang, X. 2017: Predicting and Early Monitoring Treatment Efficiency of Cervical Cancer Under Concurrent Chemoradiotherapy by Intravoxel Incoherent Motion Magnetic Resonance Imaging. Journal of Computer Assisted Tomography 41(3): 422-429

Guo, Z.; Xiao, D.; Li, D.; Wang, X.; Wang, Y.; Yan, T.; Wang, Z. 2016: Predicting and Evaluating the Epidemic Trend of Ebola Virus Disease in the 2014-2015 Outbreak and the Effects of Intervention Measures. PloS one 11(4): e0152438

Reilly, J.P. 2016: Predicting and Eventually Preventing the Future: Sepsis Risk in Community-Dwelling Adults. Critical Care Medicine 44(7): 1425-1426

Fukuda, K.; Woodman, G.F. 2015: Predicting and Improving Recognition Memory Using Multiple Electrophysiological Signals in Real time. Psychological Science 26(7): 1026-1037

Juhász, C. 2016: Predicting and Preventing Epilepsy in Sturge-Weber Syndrome?. Pediatric Neurology Briefs 30(11): 43

Erickson, B.J.; Chalmers, P.N.; Bush-Joseph, C.A.; Romeo, A.A. 2016: Predicting and Preventing Injury in Major League Baseball. American Journal of Orthopedics 45(3): 152-156

Shi, C.; Yu, C.-H.; Zhang, W. 2016: Predicting and Screening Dielectric Transitions in a Series of Hybrid Organic-Inorganic Double Perovskites via an Extended Tolerance Factor Approach. Angewandte Chemie 55(19): 5798-5802

Chow, M.L.; Troussicot, L.; Martin, M.; Doumèche, B.; Guillière, F.; Lancelin, J-Marc. 2016: Predicting and Understanding the Enzymatic Inhibition of Human Peroxiredoxin 5 by 4-Substituted Pyrocatechols by Combining Funnel Metadynamics, Solution NMR, and Steady-State Kinetics. Biochemistry 55(24): 3469-3480

Mehmandar, M.; Soori, H.; Mehrabi, Y. 2016: Predicting and analyzing the trend of traffic accidents deaths in Iran in 2014 and 2015. International Journal of Critical Illness and Injury Science 6(2): 74-78

Michielsen, A.; Kalantari, Z.; Lyon, S.W.; Liljegren, E. 2016: Predicting and communicating flood risk of transport infrastructure based on watershed characteristics. Journal of Environmental Management 182: 505-518

Ratelle, J.T.; Kelm, D.J.; Halvorsen, A.J.; West, C.P.; Oxentenko, A.S. 2015: Predicting and communicating risk of clinical deterioration: an observational cohort study of internal medicine residents. Journal of general internal medicine 30(4): 448-453

Wang, D.-P.; Lai, J.-C.; Lai, H.-Y.; Mo, S.-R.; Zeng, K.-Y.; Li, C.-H.; Zuo, J.-L. 2018: Distinct Mechanical and Self-Healing Properties in two Polydimethylsiloxane Coordination Polymers with Fine-Tuned Bond Strength. Inorganic Chemistry 57(6): 3232-3242

Cai, H.; Lilburn, T.G.; Hong, C.; Gu, J.; Kuang, R.; Wang, Y. 2015: Predicting and exploring network components involved in pathogenesis in the malaria parasite via novel subnetwork alignments. Bmc Systems Biology 9(Suppl 4): S1

Lee-Dadswell, G.R. 2015: Predicting and identifying finite-size effects in current spectra of one-dimensional oscillator chains. Physical Review. e Statistical Nonlinear and Soft Matter Physics 91(1): 012138

Yue, C. 2015: Predicting and influencing training success: spatial abilities and instructional design. Medical Education 49(11): 1054-1055

Van Leer, E.; Connor, N.P. 2015: Predicting and influencing voice therapy adherence using social-cognitive factors and mobile video. American Journal of Speech-Language Pathology 24(2): 164-176

Mirzabeigi, M.N.; Wilson, A.J.; Fischer, J.P.; Basta, M.; Kanchwala, S.; Kovach, S.J.; Serletti, J.M.; Wu, L.C. 2015: Predicting and managing donor-site wound complications in abdominally based free flap breast reconstruction: improved outcomes with early reoperative closure. Plastic and Reconstructive Surgery 135(1): 14-23

Smith, A.N.; Christian, M.P.; Firebaugh, S.L.; Cooper, G.W.; Jamieson, B.G. 2015: Predicting and managing heat dissipation from a neural probe. Biomedical Microdevices 17(4): 81

Miller, A.; Mandeville, J. 2016: Predicting and measuring fluid responsiveness with echocardiography. Echo Research and Practice 3(2): G1-G12

Janowski, M.; Walczak, P.; Pearl, M.S. 2016: Predicting and optimizing the territory of blood-brain barrier opening by superselective intra-arterial cerebral infusion under dynamic susceptibility contrast MRi guidance. Journal of Cerebral Blood Flow and Metabolism: Official Journal of the International Society of Cerebral Blood Flow and Metabolism 36(3): 569-575

Linder, K.E.; Nicolau, D.P.; Nailor, M.D. 2016: Predicting and preventing antimicrobial resistance utilizing pharmacodynamics: Part I gram positive bacteria. Expert Opinion on Drug Metabolism and Toxicology 12(3): 267-280

Rippey, J.Cr.; Carr, P.J.; Cooke, M.; Higgins, N.; Rickard, C.M. 2016: Predicting and preventing peripheral intravenous cannula insertion failure in the emergency department: Clinician 'gestalt' wins again. Emergency Medicine Australasia: Ema 28(6): 658-665

Covert, K.L.; Fleming, J.N.; Staino, C.; Casale, J.P.; Boyle, K.M.; Pilch, N.A.; Meadows, H.B.; Mardis, C.R.; McGillicuddy, J.W.; Nadig, S.; Bratton, C.F.; Chavin, K.D.; Baliga, P.K.; Taber, D.J. 2016: Predicting and preventing readmissions in kidney transplant recipients. Clinical Transplantation 30(7): 779-786

Raza, K.; Klareskog, L.; Holers, V.Michael. 2016: Predicting and preventing the development of rheumatoid arthritis. Rheumatology 55(1): 1-3

Gizzi, M.; Oberic, L.; Massard, C.; Poterie, A.; Le Teuff, G.; Loriot, Y.; Albiges, L.; Baciarello, G.; Michels, J.; Bossi, A.; Blanchard, P.; Escudier, B.; Fizazi, K. 2016: Predicting and preventing thromboembolic events in patients receiving cisplatin-based chemotherapy for germ cell tumours. European Journal of Cancer 69: 151-157

Kim, M.; Chu, A.; Khan, Y.; Malik, S. 2015: Predicting and preventing vascular complications following percutaneous coronary intervention in women. Expert Review of Cardiovascular Therapy 13(2): 163-172

Kimchi, G.; Stlylianou, P.; Wohl, A.; Hadani, M.; Cohen, Z.R.; Zauberman, J.; Feldman, Z.; Spiegelmann, R.; Nissim, O.; Zivly, Z.; Penn, M.; Harnof, S. 2016: Predicting and reducing cranioplasty infections by clinical, radiographic and operative parameters - a historical cohort study. Journal of Clinical Neuroscience: Official Journal of the Neurosurgical Society of Australasia 34: 182-186

Turner, S. 2016: Predicting and reducing risk of exacerbations in children with asthma in the primary care setting: current perspectives. Pragmatic and Observational Research 7: 33-39

Mehta, V.K. 2016: Predicting and reducing risk of radiation pneumonitis in patients with non-small cell lung cancer: A literature review. Journal of Clinical Oncology 23(16_Suppl): 7344-7344

Deary, I.J.; Brett, C.E. 2015: Predicting and retrodicting intelligence between childhood and old age in the 6-Day Sample of the Scottish Mental Survey 1947. Intelligence 50: 1-9

Peñaranda, D.A.; Simonetti, J.A. 2015: Predicting and setting conservation priorities for Bolivian mammals based on biological correlates of the risk of decline. Conservation Biology: the Journal of the Society for Conservation Biology 29(3): 834-843

Becker, C.; Bouvier, E.; Ghestem, A.; Siyoucef, S.; Claverie, D.; Camus, F.ço.; Bartolomei, F.; Benoliel, J.-J.; Bernard, C. 2015: Predicting and treating stress-induced vulnerability to epilepsy and depression. Annals of Neurology 78(1): 128-136

Breed, G.A.; Golson, E.A.; Tinker, M.T. 2017: Predicting animal home-range structure and transitions using a multistate Ornstein-Uhlenbeck biased random walk. Ecology 98(1): 32-47

Progiou, A.G.; Ziomas, I.C. 2015: Predicting annual average particulate concentration in urban areas. Science of the Total Environment 532: 353-359

Peleg, M.; Kim, A.D.; Normand, M.D. 2015: Predicting anthocyanins' isothermal and non-isothermal degradation with the endpoints method. Food Chemistry 187: 537-544

Yang, X.; Liu, H.; Yang, Q.; Liu, J.; Chen, J.; Shi, L. 2016: Predicting anti-androgenic activity of bisphenols using molecular docking and quantitative structure-activity relationships. Chemosphere 163: 373-381

Gágyor, I.ó; Haasenritter, J.ör.; Bleidorn, J.; McIsaac, W.; Schmiemann, G.; Hummers-Pradier, E.; Himmel, W. 2016: Predicting antibiotic prescription after symptomatic treatment for urinary tract infection: development of a model using data from an RCT in general practice. British Journal of General Practice: the Journal of the Royal College of General Practitioners 66(645): E234-E240

Bianchi, R.; Schonfeld, I.S.; Laurent, E. 2015: Predicting antidepressant treatment without controlling for depression is ill-advised. Journal of Psychiatric Research 69: 180-181

Garcia-Domenech, R.; Zanni, R.; Galvez-Llompart, M.; Galvez, J. 2015: Predicting antiprotozoal activity of benzyl phenyl ether diamine derivatives through QSAR multi-target and molecular topology. Molecular Diversity 19(2): 357-366

Tseilikman, O.B.; Kozochkin, D.A.; Manukhina, E.B.; Downey, H.Fred.; Misharina, M.E.; Komelkova, M.V.; Nikitina, A.A.; Golodnii, S.V.; Dodohova, M.A.; Tseilikman, V.E. 2016: Predicting anxiety responses to halogenated glucocorticoid drugs using the hexobarbital sleep time test. Stress 19(4): 390-394

Yourganov, G.; Smith, K.G.; Fridriksson, J.; Rorden, C. 2015: Predicting aphasia type from brain damage measured with structural MRi. Cortex; a Journal Devoted to the Study of the Nervous System and Behavior 73: 203-215

Scafoglieri, A.; Clarys, J.P.; Bauer, J.ür.M.; Verlaan, S.; Van Malderen, L.; Vantieghem, S.; Cederholm, T.; Sieber, C.C.; Mets, T.; Bautmans, I. 2017: Predicting appendicular lean and fat mass with bioelectrical impedance analysis in older adults with physical function decline - the PROVIDE study. Clinical Nutrition 36(3): 869-875

Valle, A.; Pan, I.; Regueiro, B.; Suárez, N.; Tuero, E.án.; Nunes, A.R. 2015: Predicting approach to homework in Primary school students. Psicothema 27(4): 334-340

Basant, N.; Gupta, S.; Singh, K.P. 2015: Predicting aquatic toxicities of chemical pesticides in multiple test species using nonlinear QSTR modeling approaches. Chemosphere 139: 246-255

Templeman, M.A.; Kingsford, M.J. 2015: Predicting aqueous copper and zinc accumulation in the upside-down jellyfish Cassiopea maremetens through the use of biokinetic models. Environmental Monitoring and Assessment 187(7): 416

Ho, S.H.; Speldewinde, P.; Cook, A. 2017: Predicting arboviral disease emergence using Bayesian networks: a case study of dengue virus in Western Australia. Epidemiology and Infection 145(1): 54-66

Boulain, T.; Garot, D.; Vignon, P.; Lascarrou, J.-B.; Benzekri-Lefevre, D.; Dequin, P.-F. 2016: Predicting arterial blood gas and lactate from central venous blood analysis in critically ill patients: a multicentre, prospective, diagnostic accuracy study. British Journal of Anaesthesia 117(3): 341-349

Zheng, T.; Zhang, X.; Wang, Y.; Yu, X. 2016: Predicting associations between micro RNAs and target genes in breast cancer by bioinformatics analyses. Oncology Letters 12(2): 1067-1073

Luo, G.; Stone, B.L.; Fassl, B.; Maloney, C.G.; Gesteland, P.H.; Yerram, S.R.; Nkoy, F.L. 2015: Predicting asthma control deterioration in children. Bmc Medical Informatics and Decision Making 15: 84

Killane, I.; Sulaiman, I.; MacHale, E.; Breathnach, A.; Taylor, T.E.; Holmes, M.S.; Reilly, R.B.; Costello, R.W. 2016: Predicting asthma exacerbations employing remotely monitored adherence. Healthcare Technology Letters 3(1): 51-55

Konradsen, J.R.; Skantz, E.; Nordlund, B.ör.; Lidegran, M.; James, A.; Ono, J.; Ohta, S.; Izuhara, K.; Dahlén, S.-E.; Alving, K.; Hedlin, G. 2015: Predicting asthma morbidity in children using proposed markers of Th2-type inflammation. Pediatric Allergy and Immunology: Official Publication of the European Society of Pediatric Allergy and Immunology 26(8): 772-779

Sears, M.R. 2015: Predicting asthma outcomes. Journal of Allergy and Clinical Immunology 136(4): 829

Ram, S.; Zhang, W.; Williams, M.; Pengetnze, Y. 2015: Predicting asthma-related emergency department visits using big data. IEEE Journal of Biomedical and Health Informatics 19(4): 1216-1223

Ruiz, M.C.; Haapanen, S.; Tolvanen, A.; Robazza, C.; Duda, J.L. 2017: Predicting athletes' functional and dysfunctional emotions: The role of the motivational climate and motivation regulations. Journal of Sports Sciences 35(16): 1598-1606

Winkle, R.A.; Jarman, J.W.E.; Mead, R.Hardwin.; Engel, G.; Kong, M.H.; Fleming, W.; Patrawala, R.A. 2016: Predicting atrial fibrillation ablation outcome: The CAAP-AF score. Heart rhythm 13(11): 2119-2125

Freitas-Murrell, B.; Swift, J.K. 2015: Predicting attitudes toward seeking professional psychological help among Alaska Natives. American Indian and Alaska Native Mental Health Research 22(3): 21-35

Mendonça, C.; Escher, A.; van de Par, S.; Colonius, H. 2015: Predicting auditory space calibration from recent multisensory experience. Experimental Brain Research 233(7): 1983-1991

Eliakim-Raz, N.; Bates, D.W.; Leibovici, L. 2015: Predicting bacteraemia in validated models--a systematic review. Clinical Microbiology and Infection: the Official Publication of the European Society of Clinical Microbiology and Infectious Diseases 21(4): 295-301

José, R.J.; Brown, J.S. 2016: Predicting bacteraemia or rapid identification of the causative pathogen in community acquired pneumonia: where should the priority lie?. European Respiratory Journal 48(3): 619-622

Larsen, P.; Dai, Y.; Collart, F.R. 2015: Predicting bacterial community assemblages using an artificial neural network approach. Methods in Molecular Biology 1260: 33-43

Galarz, L.A.; Fonseca, G.G.; Prentice, C. 2016: Predicting bacterial growth in raw, salted, and cooked chicken breast fillets during storage. Food Science and Technology International 22(6): 461-474

Ding, H.; Liang, Z-Yong.; Guo, F-Biao.; Huang, J.; Chen, W.; Lin, H. 2016: Predicting bacteriophage proteins located in host cell with feature selection technique. Computers in Biology and Medicine 71: 156-161

Wikstrom, E.A.; McKeon, P.O. 2017: Predicting balance improvements following STARS treatments in chronic ankle instability participants. Journal of Science and Medicine in Sport 20(4): 356-361

Meyer, A.D.; Stevens, D.F.; Blackwood, J.C. 2016: Predicting bat colony survival under controls targeting multiple transmission routes of white-nose syndrome. Journal of Theoretical Biology 409: 60-69

Ye, Z.; Rae, C.L.; Nombela, C.; Ham, T.; Rittman, T.; Jones, P.S.; Rodríguez, P.V.áz.; Coyle-Gilchrist, I.; Regenthal, R.; Altena, E.; Housden, C.R.; Maxwell, H.; Sahakian, B.J.; Barker, R.A.; Robbins, T.W.; Rowe, J.B. 2016: Predicting beneficial effects of atomoxetine and citalopram on response inhibition in Parkinson's disease with clinical and neuroimaging measures. Human Brain Mapping 37(3): 1026-1037

Gireud-Goss, M.; Reyes, S.; Wilson, M.; Farley, M.; Memarzadeh, K.; Srinivasan, S.; Sirisaengtaksin, N.; Yamashita, S.; Tsunoda, S.; Lang, F.F.; Waxham, M.N.; Bean, A.J. 2018: Distinct mechanisms enable inward or outward budding from late endosomes/multivesicular bodies. Experimental Cell Research 372(1): 1-15

Regan, M.M. 2015: Predicting benefit of endocrine therapy for early breast cancer. Breast 24(Suppl 2): S129-S131

Horowitz-Kraus, T.; Eaton, K.; Farah, R.; Hajinazarian, A.; Vannest, J.; Holland, S.K. 2015: Predicting better performance on a college preparedness test from narrative comprehension at the age of 6 years: An fMRI study. Brain research 1629: 54-62

Klein, B.P.; Paffen, C.L.E.; Pas, S.F.T.; Dumoulin, S.O. 2016: Predicting bias in perceived position using attention field models. Journal of Vision 16(7): 15

Grainger, K.; Dodson, Z.; Korff, T. 2017: Predicting bicycle setup for children based on anthropometrics and comfort. Applied Ergonomics 59(Part A): 449-459

Yang, J.; Pak, Y.E.; Lee, T.-R. 2016: Predicting bifurcation angle effect on blood flow in the microvasculature. Microvascular Research 108: 22-28

Plauška, A.; Borst, J.G.; van der Heijden, M. 2016: Predicting binaural responses from monaural responses in the gerbil medial superior olive. Journal of Neurophysiology 115(6): 2950-2963

Chabot-Leclerc, A.; MacDonald, E.N.; Dau, T. 2016: Predicting binaural speech intelligibility using the signal-to-noise ratio in the envelope power spectrum domain. Journal of the Acoustical Society of America 140(1): 192

Basant, N.; Gupta, S.; Singh, K.P. 2016: Predicting binding affinities of diverse pharmaceutical chemicals to human serum plasma proteins using QSPR modelling approaches. Sar and Qsar in Environmental Research 27(1): 67-85

Grudinin, S.; Kadukova, M.; Eisenbarth, A.; Marillet, S.; Cazals, F.éd.ér. 2016: Predicting binding poses and affinities for protein - ligand complexes in the 2015 D3R Grand Challenge using a physical model with a statistical parameter estimation. Journal of Computer-Aided Molecular Design 30(9): 791-804

Fernandez, L.A.; Gschwend, P.M. 2015: Predicting bioaccumulation of polycyclic aromatic hydrocarbons in soft-shelled clams (Mya arenaria) using field deployments of polyethylene passive samplers. Environmental Toxicology and Chemistry 34(5): 993-1000

Anighoro, A.; de la Vega de León, A.; Bajorath, J.ür. 2016: Predicting bioactive conformations and binding modes of macrocycles. Journal of Computer-Aided Molecular Design 30(10): 841-849

Lin, S.; Wang, X.; Chao, Y.; He, Y.; Liu, M. 2016: Predicting biofilm thickness and biofilm viability based on the concentration of carbon-nitrogen-phosphorus by support vector regression. Environmental Science and Pollution Research International 23(1): 418-425

Muenster, U.; Mueck, W.; van der Mey, D.; Schlemmer, K.-H.; Greschat-Schade, S.; Haerter, M.; Pelzetter, C.; Pruemper, C.; Verlage, J.; Göller, A.H.; Ohm, A. 2016: Predicting biopharmaceutical performance of oral drug candidates - Extending the volume to dissolve applied dose concept. European Journal of Pharmaceutics and Biopharmaceutics: Official Journal of Arbeitsgemeinschaft für Pharmazeutische Verfahrenstechnik E.V 102: 191-201

Kadowaki, K.; Barbera, C.G.; Godsoe, W.; Delsuc, F.éd.ér.; Mouquet, N. 2016: Predicting biotic interactions and their variability in a changing environment. Biology Letters 12(5)

Cole, E.F.; Long, P.R.; Zelazowski, P.; Szulkin, M.; Sheldon, B.C. 2015: Predicting bird phenology from space: satellite-derived vegetation green-up signal uncovers spatial variation in phenological synchrony between birds and their environment. Ecology and Evolution 5(21): 5057-5074

Cox, P.G.; Rinderknecht, A.és.; Blanco, R.E. 2015: Predicting bite force and cranial biomechanics in the largest fossil rodent using finite element analysis. Journal of Anatomy 226(3): 215-223

Cook, M.; Smart, N.A.; Van der Touw, T. 2016: Predicting blood flow responses to rhythmic handgrip exercise from one second isometric contractions. Physiological Research 65(4): 581-589

Villa, L.; Sun, D.; Denhaerynck, K.; Vancayzeele, S.; Brié, H.; Hermans, C.; Aerts, A.; Levengood, M.; MacDonald, K.; Abraham, I. 2015: Predicting blood pressure outcomes using single-item physician-administered measures: a retrospective pooled analysis of observational studies in Belgium. British Journal of General Practice: the Journal of the Royal College of General Practitioners 65(630): E9-15

Schneider, C.; Boddy, A.P.; Fukuta, J.; Groom, W.D.; Streets, C.G. 2014: Predicting blood transfusion in patients undergoing minimally invasive oesophagectomy. International Journal of Surgery 12(12): 1342-1347

Shackelford, S.; Yang, S.; Hu, P.; Miller, C.; Anazodo, A.; Galvagno, S.; Wang, Y.; Hartsky, L.; Fang, R.; Mackenzie, C.; Anazodo, A.; Barker, S.; Blenko, J.; Chang, C.-I.; Chen, H.; Dinardo, T.; DuBose, J.; Fang, R.; Fouche, Y.; Goetz, L.; Grissom, T.; Giustina, V.; Hagegeorge, G.; Herrera, A.; Hess, J.; Hu, P.; Imle, C.; Mackenzie, C.; Menaker, J.; Murdock, K.; Narayan, M.; Oates, T.; Pasley, J.; Saccicchio, S.; Scalea, T.; Shackelford, S.; Sikorski, R.; Smith, L.; Stansbury, L.; Stein, D.; Stephens, C. 2015: Predicting blood transfusion using automated analysis of pulse oximetry signals and laboratory values. Journal of Trauma and Acute Care Surgery 79(4 Suppl 2: S175-S180

Andrew, R.; Tiggemann, M.; Clark, L. 2016: Predicting body appreciation in young women: An integrated model of positive body image. Body Image 18: 34-42

Wu, C.-S.; Chen, Y.-Y.; Chuang, C.-L.; Chiang, L.-M.; Dwyer, G.B.; Hsu, Y.-L.; Huang, A.-C.; Lai, C.-L.; Hsieh, K.-C. 2015: Predicting body composition using foot-to-foot bioelectrical impedance analysis in healthy Asian individuals. Nutrition Journal 14: 52

Kouda, K.; Ohara, K.; Nakamura, H.; Fujita, Y.; Iki, M. 2017: Predicting bone mineral acquisition during puberty: data from a 3-year follow-up study in Hamamatsu, Japan. Journal of Bone and Mineral Metabolism 35(2): 185-191

Marini, C.; Fossa, F.; Paoli, C.; Bellingeri, M.; Gnone, G.; Vassallo, P. 2015: Predicting bottlenose dolphin distribution along Liguria coast (northwestern Mediterranean Sea) through different modeling techniques and indirect predictors. Journal of Environmental Management 150: 9-20

Fernandino, L.; Humphries, C.J.; Seidenberg, M.S.; Gross, W.L.; Conant, L.L.; Binder, J.R. 2015: Predicting brain activation patterns associated with individual lexical concepts based on five sensory-motor attributes. Neuropsychologia 76: 17-26

Liem, F.; Varoquaux, G.ël.; Kynast, J.; Beyer, F.; Kharabian Masouleh, S.; Huntenburg, J.M.; Lampe, L.; Rahim, M.; Abraham, A.; Craddock, R.C.; Riedel-Heller, S.; Luck, T.; Loeffler, M.; Schroeter, M.L.; Witte, A.V.; Villringer, A.; Margulies, D.S. 2017: Predicting brain-age from multimodal imaging data captures cognitive impairment. Neuroimage 148: 179-188

Thomas, M.; De Brabanter, K.; Suykens, J.A.K.; De Moor, B. 2014: Predicting breast cancer using an expression values weighted clinical classifier. Bmc Bioinformatics 15: 411

Chowdhury, S.; Mazumder, A.J.; Husain, T. 2016: Predicting bromide incorporation in a chlorinated indoor swimming pool. Environmental Science and Pollution Research International 23(12): 12174-12184

Altman, R.B. 2015: Predicting cancer drug response: advancing the DREAM. Cancer Discovery 5(3): 237-238

Zhou, B.; Sun, Q.; Kong, D.-X. 2016: Predicting cancer-relevant proteins using an improved molecular similarity ensemble approach. Oncotarget 7(22): 32394-32407

Coburn, C. 2016: Predicting cancer: can we? Should we?. LANCET. Oncology 17(11): 1494

Wu, X.; Zhang, Q.; Wang, H.; Hu, J. 2015: Predicting carcinogenicity of organic compounds based on CPDB. Chemosphere 139: 81-90

Sato, A. 2015: Predicting cardiac and all-cause death in asymptomatic patients on hemodialysis – importance of training in interpretation of β-methyl iodophenyl-pentadecanoic acid single-photon emission computed tomography (BMIPP SPECT) imaging. Circulation Journal: Official Journal of the Japanese Circulation Society 79(1): 47-48

Yoshinaga, K. 2017: Predicting cardiac events using ventricular dyssynchrony in patients who received implantable cardioverter defibrillators: Are more treatment options required?. Journal of Nuclear Cardiology: Official Publication of the American Society of Nuclear Cardiology 24(1): 130-133

Jorge-Monjas, P.; Bustamante-Munguira, J.; Lorenzo, M.; Heredia-Rodríguez, M.ía.; Fierro, I.; Gómez-Sánchez, E.; Hernandez, A.; Álvarez, F.J.; Bermejo-Martin, J.ús.F.; Gómez-Pesquera, E.ía.; Gómez-Herreras, J.é I.; Tamayo, E. 2016: Predicting cardiac surgery-associated acute kidney injury: the CRATE score. Journal of Critical Care 31(1): 130-138

Markusse, I.M.; Meijs, J.; de Boer, B.; Bakker, J.A.; Schippers, H.Pascal.C.; Schouffoer, A.A.; Ajmone Marsan, N.; Kroft, L.J.M.; Ninaber, M.K.; Huizinga, T.W.J.; de Vries-Bouwstra, J.K. 2017: Predicting cardiopulmonary involvement in patients with systemic sclerosis: complementary value of nailfold videocapillaroscopy patterns and disease-specific autoantibodies. Rheumatology 56(7): 1081-1088

Pinsky, M.R.; Clermont, G.; Hravnak, M. 2016: Predicting cardiorespiratory instability. Critical Care 20: 70

Holt, T. 2016: Predicting cardiovascular disease. Bmj 353: I2621

Leong, D.P.; Teo, K.K. 2015: Predicting cardiovascular disease from handgrip strength: the potential clinical implications. Expert Review of Cardiovascular Therapy 13(12): 1277-1279

Brawner, C.A.; Ahmed, H.M. 2015: Predicting cardiovascular events … How FIT is our crystal ball?. Atherosclerosis 241(2): 741-742

Gore, M.Odette.; McGuire, D.K.; Lingvay, I.; Rosenstock, J. 2015: Predicting cardiovascular risk in type 2 diabetes: the heterogeneity challenges. Current Cardiology Reports 17(7): 607

Katsavos, S.; Artemiadis, A.K.; Zacharis, M.; Argyrou, P.; Theotoka, I.; Chrysovitsanou, C.; Anagnostouli, M. 2017: Predicting caregiving status and caregivers' burden in multiple sclerosis. A short report. Neurological Research 39(1): 13-15

Platteel, T.N.; Leverstein-van Hall, M.A.; Cohen Stuart, J.W.; Thijsen, S.F.T.; Mascini, E.M.; van Hees, B.C.; Scharringa, J.; Fluit, A.C.; Bonten, M.J.M. 2015: Predicting carriage with extended-spectrum beta-lactamase-producing bacteria at hospital admission: a cross-sectional study. Clinical Microbiology and Infection: the Official Publication of the European Society of Clinical Microbiology and Infectious Diseases 21(2): 141-146

Achiron, A.; Haddad, F.; Gerra, M.; Bartov, E.; Burgansky-Eliash, Z. 2016: Predicting cataract surgery time based on preoperative risk assessment. European Journal of Ophthalmology 26(3): 226-229

Weissmann, H.; Shnerb, N.M. 2016: Predicting catastrophic shifts. Journal of Theoretical Biology 397: 128-134

Kim, D.; Li, R.; Dudek, S.M.; Ritchie, M.D. 2015: Predicting censored survival data based on the interactions between meta-dimensional omics data in breast cancer. Journal of Biomedical Informatics 56: 220-228

Kikuchi, M.; Nakagawa, M.; Tone, S.; Saito, H.; Niino, T.; Nagasawa, N.; Sawai, J. 2016: Predicting changes in aquatic toxicity of chemicals resulting from solvent or dispersant use as vehicle. Chemosphere 154: 34-39

Kang, W.H.; Morrison, B. 2015: Predicting changes in cortical electrophysiological function after in vitro traumatic brain injury. Biomechanics and Modeling in Mechanobiology 14(5): 1033-1044

Masoud, M.I.; Marghalani, H.Y.A.; Bamashmous, M.; Alamoudi, N.M.; El Derwi, D.; Masoud, I.M.; Allareddy, V.; Gowharji, N.F. 2015: Predicting changes in mandibular length and total anterior facial height using IGF-1, cervical stage, skeletal classification, and gender. Progress in Orthodontics 16: 7

Tawara, D.; Nagura, K. 2017: Predicting changes in mechanical properties of trabecular bone by adaptive remodeling. Computer Methods in Biomechanics and Biomedical Engineering 20(4): 415-425

Ye, Z.J.; Qiu, H.Z.; Li, P.F.; Liang, M.Z.; Zhu, Y.F.; Zeng, Z.; Hu, G.Y.; Wang, S.N.; Quan, X.M. 2017: Predicting changes in quality of life and emotional distress in Chinese patients with lung, gastric, and colon-rectal cancer diagnoses: the role of psychological resilience. Psycho-Oncology 26(6): 829-835

Solomon, J.Wes.; Nielsen, R.D. 2015: Predicting changes in systolic blood pressure using longitudinal patient records. Journal of Biomedical Informatics 58 Suppl: S197-S202

Ehrlén, J.; Morris, W.F. 2015: Predicting changes in the distribution and abundance of species under environmental change. Ecology Letters 18(3): 303-314

Niehren, J.; Versari, C.; John, M.; Coutte, F.ço.; Jacques, P. 2016: Predicting changes of reaction networks with partial kinetic information. Bio Systems 149: 113-124

Hamilton, F.; Berry, T.; Sauer, T. 2015: Predicting chaotic time series with a partial model. Physical Review. e Statistical Nonlinear and Soft Matter Physics 92(1): 010902

Lee, B.; Kullman, S.W.; Yost, E.E.; Meyer, M.T.; Worley-Davis, L.; Williams, C.M.; Reckhow, K.H. 2015: Predicting characteristics of rainfall driven estrogen runoff and transport from swine AFO spray fields. Science of the Total Environment 532: 571-580

Gong, P.; Nan, X.; Barker, N.D.; Boyd, R.E.; Chen, Y.; Wilkins, D.E.; Johnson, D.R.; Suedel, B.C.; Perkins, E.J. 2016: Predicting chemical bioavailability using microarray gene expression data and regression modeling: a tale of three explosive compounds. Bmc Genomics 17: 205

Alves, V.M.; Muratov, E.; Fourches, D.; Strickland, J.; Kleinstreuer, N.; Andrade, C.H.; Tropsha, A. 2015: Predicting chemically-induced skin reactions. Part I: QSAR models of skin sensitization and their application to identify potentially hazardous compounds. Toxicology and Applied Pharmacology 284(2): 262-272

Alves, V.M.; Muratov, E.; Fourches, D.; Strickland, J.; Kleinstreuer, N.; Andrade, C.H.; Tropsha, A. 2015: Predicting chemically-induced skin reactions. Part II: QSAR models of skin permeability and the relationships between skin permeability and skin sensitization. Toxicology and Applied Pharmacology 284(2): 273-280

Savage, R.S.; Yuan, Y. 2016: Predicting chemoinsensitivity in breast cancer with 'omics/digital pathology data fusion. Royal Society Open Science 3(2): 140501

Meng, X.; Wang, G.; Guan, R.; Jia, X.; Gao, W.; Wu, J.; Yu, J.; Liu, P.; Yu, Y.; Sun, W.; Dong, H.; Fu, S. 2015: Predicting chemosensitivity to gemcitabine and cisplatin based on gene polymorphisms and mRNA expression in non-small-cell lung cancer cells. Pharmacogenomics 16(1): 23-34

Li, Y.; Sulik, M.J.; Eisenberg, N.; Spinrad, T.L.; Lemery-Chalfant, K.; Stover, D.A.; Verrelli, B.C. 2016: Predicting childhood effortful control from interactions between early parenting quality and children's dopamine transporter gene haplotypes. Development and Psychopathology 28(1): 199-212

Lourenço-Matharu, L.; Papineni McIntosh, A.; Lo, J.W. 2016: Predicting children's behaviour during dental treatment under oral sedation. European Archives of Paediatric Dentistry: Official Journal of the European Academy of Paediatric Dentistry 17(3): 157-163

Huang, J.; Marco, E.; Pinello, L.; Yuan, G.-C. 2015: Predicting chromatin organization using histone marks. Genome Biology 16: 162

Jacobsohn, D.A. 2016: Predicting chronic GVHD outcomes: are we there yet?. Blood 127(1): 14-15

Carrier, J-Daniel.; Roberge, P.; Courteau, J.; Vanasse, A. 2016: Predicting chronic benzodiazepine use in adults with depressive disorder: Retrospective cohort study using administrative data in Quebec. Canadian Family Physician Medecin de Famille Canadien 62(8): E473-E483

Taylor, N.S.; Kirwan, J.A.; Johnson, C.; Yan, N.D.; Viant, M.R.; Gunn, J.M.; McGeer, J.C. 2016: Predicting chronic copper and nickel reproductive toxicity to Daphnia pulex-pulicaria from whole-animal metabolic profiles. Environmental Pollution 212: 325-329

Panken, G.; Hoekstra, T.; Verhagen, A.; van Tulder, M.; Twisk, J.; Heymans, M.W. 2016: Predicting chronic low-back pain based on pain trajectories in patients in an occupational setting: an exploratory analysis. Scandinavian Journal of Work Environment and Health 42(6): 520-527

Basavanhally, A.; Viswanath, S.; Madabhushi, A. 2015: Predicting classifier performance with limited training data: applications to computer-aided diagnosis in breast and prostate cancer. Plos one 10(5): E0117900

Kelly, P.J.; Leung, J.; Deane, F.P.; Lyons, G.C.B. 2016: Predicting client attendance at further treatment following drug and alcohol detoxification: Theory of Planned Behaviour and Implementation Intentions. Drug and Alcohol Review 35(6): 678-685

Moor, H.; Hylander, K.; Norberg, J. 2015: Predicting climate change effects on wetland ecosystem services using species distribution modeling and plant functional traits. Ambio 44(Suppl 1): S113-S126

Hirsch, F.R.; Franklin, W.A.; McCoy, J.; Cappuzzo, F.; Varella-Garcia, M.; Witta, S.E.; Gumerlock, P.; West, H.; Gandara, D.R.; Bunn, P.A. 2016: Predicting clinical benefit from EGFR TKIs: Not all EGFR mutations are equal. Journal of Clinical Oncology 24(18_Suppl): 7072-7072

Hayward, G.N.; Vincent, C.; Lasserson, D.S. 2017: Predicting clinical deterioration after initial assessment in out-of-hours primary care: a retrospective service evaluation. British Journal of General Practice: the Journal of the Royal College of General Practitioners 67(654): E78-E85

Fenerty, K.E.; Folio, L.R.; Patronas, N.J.; Marté, J.L.; Gulley, J.L.; Heery, C.R. 2016: Predicting clinical outcomes in chordoma patients receiving immunotherapy: a comparison between volumetric segmentation and RECIST. Bmc Cancer 16(1): 672

Tong, M.J.; Huynh, T.T.; Siripongsakun, S.; Chang, P.W.; Tong, L.T.; Ha, Y.P.; Mena, E.A.; Weissman, M.F. 2015: Predicting clinical outcomes in patients with HBsAg-positive chronic hepatitis. Hepatology International 9(4): 567-577

Shahani, L.; Darouiche, R.O. 2016: Predicting clinical outcomes in patients with negative peripheral and positive central blood culture with coagulase negative Staphylococcus species. Hospital Practice 44(4): 179-182

Bailey, D.G. 2017: Predicting clinical relevance of grapefruit-drug interactions: a complicated process. Journal of Clinical Pharmacy and Therapeutics 42(2): 125-127

Mechelli, A.; Prata, D.; Kefford, C.; Kapur, S. 2015: Predicting clinical response in people at ultra-high risk of psychosis: a systematic and quantitative review. Drug Discovery Today 20(8): 924-927

Qin, J.; Shen, H.; Zeng, L.-L.; Jiang, W.; Liu, L.; Hu, D. 2015: Predicting clinical responses in major depression using intrinsic functional connectivity. Neuroreport 26(12): 675-680

Lee, H.K.; Tang, J.W.-T.; Loh, T.P.; Oon, L.L.-E.; Koay, E.S.-C. 2015: Predicting clinical severity based on substitutions near epitope a of influenza A/H3N2. Infection Genetics and Evolution: Journal of Molecular Epidemiology and Evolutionary Genetics in Infectious Diseases 34: 292-297

Batty, R.A.; Francis, A.; Thomas, N.; Hopwood, M.; Ponsford, J.; Rossell, S.L. 2016: Predicting co-morbid traumatic brain injury and psychosis from neuropsychological profile. Schizophrenia Research 172(1-3): 143-144

Chappell, T.M.; Kennedy, G.G.; Walgenbach, J.F. 2015: Predicting codling moth (Cydia pomonella) phenology in North Carolina on the basis of temperature and improved generation turnover estimates. Pest Management Science 71(10): 1425-1432

Bolandzadeh, N.; Kording, K.; Salowitz, N.; Davis, J.C.; Hsu, L.; Chan, A.; Sharma, D.; Blohm, G.; Liu-Ambrose, T. 2015: Predicting cognitive function from clinical measures of physical function and health status in older adults. Plos one 10(3): E0119075

Foong, H.Foh.; Hamid, T.Aizan.; Ibrahim, R.; Haron, S.Azizah.; Shahar, S. 2018: Predicting cognitive function of the Malaysian elderly: a structural equation modelling approach. Aging and Mental Health 22(1): 109-120

Lopez, M.J.; Schuckers, M. 2017: Predicting coin flips: using resampling and hierarchical models to help untangle the NHL's shoot-out. Journal of Sports Sciences 35(9): 888-897

Wright, P.; Alex, A.; Pullen, F. 2016: Predicting collision-induced dissociation mass spectra: understanding the role of the mobile proton in small molecule fragmentation. Rapid Communications in Mass Spectrometry: Rcm 30(9): 1163-1175

Soguero-Ruiz, C.; Hindberg, K.; Mora-Jiménez, I.; Rojo-Álvarez, J.é L.; Skrøvseth, S.O.; Godtliebsen, F.; Mortensen, K.; Revhaug, A.; Lindsetmo, R.-O.; Augestad, K.M.; Jenssen, R. 2016: Predicting colorectal surgical complications using heterogeneous clinical data and kernel methods. Journal of Biomedical Informatics 61: 87-96

Cabral, R.G.; Chapman, C.E.; Aragona, K.M.; Clark, E.; Lunak, M.; Erickson, P.S. 2016: Predicting colostrum quality from performance in the previous lactation and environmental changes. Journal of Dairy Science 99(5): 4048-4055

Cadotte, M.W.; Arnillas, C.A.; Livingstone, S.W.; Yasui, S.-L.E. 2015: Predicting communities from functional traits. Trends in Ecology and Evolution 30(9): 510-511

Suttiwong, J.; Vongsirinavarat, M.; Chaiyawat, P.; Vachalathiti, R. 2015: Predicting community participation after spinal cord injury in Thailand. Journal of Rehabilitation Medicine 47(4): 325-329

Burbrink, F.T.; McKelvy, A.D.; Pyron, R.A.; Myers, E.A. 2015: Predicting community structure in snakes on Eastern Nearctic islands using ecological neutral theory and phylogenetic methods. PROCEEDINGS. Biological Sciences 282(1819)

Lord, J.; Whitlatch, R. 2015: Predicting competitive shifts and responses to climate change based on latitudinal distributions of species assemblages. Ecology 96(5): 1264-1274

Yang, T.J.; Goodman, K.A. 2015: Predicting complete response: is there a role for non-operative management of rectal cancer?. Journal of Gastrointestinal Oncology 6(2): 241-246

Mohn, C.E.; Kob, W. 2015: Predicting complex mineral structures using genetic algorithms. Journal of Physics. Condensed Matter: An Institute of Physics Journal 27(42): 425201

Kazanina, N. 2017: Predicting complex syntactic structure in real time: Processing of negative sentences in Russian. Quarterly Journal of Experimental Psychology 70(11): 2200-2218

Ramirez, D.; Maurice, M.J.; Caputo, P.A.; Nelson, R.J.; Kara, Ön.; Malkoç, E.; Kaouk, J.H. 2016: Predicting complications in partial nephrectomy for T1a tumours: does approach matter?. Bju International 118(6): 940-945

Tang, L.Y.; Hing, J.W.; Tang, J.Y.; Nishikawa, H.; Shahidullah, H.; Browne, F.; Chikermane, A.; Parulekar, M. 2016: Predicting complications with pretreatment testing in infantile haemangioma treated with oral propranolol. British Journal of Ophthalmology 100(7): 902-906

Kiri, L.; Boyd, D. 2015: Predicting composition-property relationships for glass ionomer cements: a multifactor central composite approach to material optimization. Journal of the Mechanical Behavior of Biomedical Materials 46: 285-291

Koch, S.; Benndorf, D.; Fronk, K.; Reichl, U.; Klamt, S. 2016: Predicting compositions of microbial communities from stoichiometric models with applications for the biogas process. Biotechnology for Biofuels 9: 17

Franquet-Griell, H.; Gómez-Canela, C.; Ventura, F.; Lacorte, S. 2015: Predicting concentrations of cytostatic drugs in sewage effluents and surface waters of Catalonia (NE Spain). Environmental Research 138: 161-172

Jiang, G.; Keller, J.; Bond, P.L.; Yuan, Z. 2016: Predicting concrete corrosion of sewers using artificial neural network. Water Research 92: 52-60

Espada, J.é P.; Morales, A.; Guillén-Riquelme, A.; Ballester, R.; Orgilés, M. 2016: Predicting condom use in adolescents: a test of three socio-cognitive models using a structural equation modeling approach. Bmc Public Health 16: 35

Platikanov, S.; Hernández, A.; González, S.; Luis Cortina, J.; Tauler, R.; Devesa, R. 2017: Predicting consumer preferences for mineral composition of bottled and tap water. Talanta 162: 1-9

Stilp, C.E.; Anderson, P.W.; Winn, M.B. 2015: Predicting contrast effects following reliable spectral properties in speech perception. Journal of the Acoustical Society of America 137(6): 3466-3476

Cabral, C.; Morgado, P.M.; Campos Costa, D.; Silveira, M. 2015: Predicting conversion from MCi to AD with FDG-PET brain images at different prodromal stages. Computers in Biology and Medicine 58: 101-109

Goonawardena, J.; Gunnarsson, R.; de Costa, A. 2015: Predicting conversion from laparoscopic to open cholecystectomy presented as a probability nomogram based on preoperative patient risk factors. American Journal of Surgery 210(3): 492-500

McGuinness, B.; Barrett, S.L.; McIlvenna, J.; Passmore, A.P.; Shorter, G.W. 2015: Predicting conversion to dementia in a memory clinic: a standard clinical approach compared with an empirically defined clustering method (latent profile analysis) for mild cognitive impairment subtyping. Alzheimer's and Dementia 1(4): 447-454

Kader, M.; Lamb, D.T.; Wang, L.; Megharaj, M.; Naidu, R. 2016: Predicting copper phytotoxicity based on pore-water pCu. Ecotoxicology 25(3): 481-490

Wang, W.-B.; Chen, S.; Wu, M.; Zhao, J. 2014: Predicting copper toxicity to Hypophthalmichthys molitrix and Ctenopharyngodon idellus based on biotic ligand model. Huan Jing Ke Xue= Huanjing Kexue 35(10): 3947-3951

Tristão, G.B.; Assunção, L.d.P.; Dos Santos, L.P.A.; Borges, C.L.; Silva-Bailão, M.G.; Soares, C.él.M.d.A.; Cavallaro, G.; Bailão, A.M. 2014: Predicting copper-, iron-, and zinc-binding proteins in pathogenic species of the Paracoccidioides genus. Frontiers in Microbiology 5: 761

Pereira, A.F.; Javaheri, B.; Pitsillides, A.A.; Shefelbine, S.J. 2015: Predicting cortical bone adaptation to axial loading in the mouse tibia. Journal of the Royal Society Interface 12(110): 0590

Cooper, E.A.; Norcia, A.M. 2015: Predicting cortical dark/bright asymmetries from natural image statistics and early visual transforms. Plos Computational Biology 11(5): E1004268

Boscardin, C.K.; Gonzales, R.; Bradley, K.L.; Raven, M.C. 2015: Predicting cost of care using self-reported health status data. Bmc Health Services Research 15: 406

Kwak, H.-C.; Kho, S. 2016: Predicting crash risk and identifying crash precursors on Korean expressways using loop detector data. Accident; Analysis and Prevention 88: 9-19

Scanlon, J.M.; Sherony, R.; Gabler, H.C. 2016: Predicting crash-relevant violations at stop sign-controlled intersections for the development of an intersection driver assistance system. Traffic Injury Prevention 17 Suppl. 1: 59-65

Braun, A.C.; Ilko, D.; Merget, B.; Gieseler, H.; Germershaus, O.; Holzgrabe, U.; Meinel, L. 2015: Predicting critical micelle concentration and micelle molecular weight of polysorbate 80 using compendial methods. European Journal of Pharmaceutics and Biopharmaceutics: Official Journal of Arbeitsgemeinschaft für Pharmazeutische Verfahrenstechnik E.V 94: 559-568

Weiss, V.C. 2015: Predicting critical temperatures of ionic and non-ionic fluids from thermophysical data obtained near the melting point. Journal of Chemical Physics 143(14): 144503

Zhang, Y. 2014: Predicting critical temperatures of iron(II) spin crossover materials: density functional theory plus U approach. Journal of Chemical Physics 141(21): 214703

Zurek, E.; Grochala, W. 2015: Predicting crystal structures and properties of matter under extreme conditions via quantum mechanics: the pressure is on. Physical Chemistry Chemical Physics: Pccp 17(5): 2917-2934

Chen, T.; Ma, Y.; Wang, Y. 2015: Predicting cumulative risk of disease onset by redistributing weights. Statistics in Medicine 34(16): 2427-2443

Barlett, C.; Chamberlin, K.; Witkower, Z. 2017: Predicting cyberbullying perpetration in emerging adults: a theoretical test of the Barlett Gentile Cyberbullying Model. Aggressive Behavior 43(2): 147-154

Diaz-Rodriguez, S.; Bozada, S.M.; Phifer, J.R.; Paluch, A.S. 2016: Predicting cyclohexane/water distribution coefficients for the SAMPL5 challenge using MOSCED and the SMD solvation model. Journal of Computer-Aided Molecular Design 30(11): 1007-1017

Jones, D.E.; Ghandehari, H.; Facelli, J.C. 2015: Predicting cytotoxicity of PAMAM dendrimers using molecular descriptors. Beilstein Journal of Nanotechnology 6: 1886-1896

Tian, D.; Zheng, W.; He, G.; Zheng, Y.; Andersen, M.E.; Tan, H.; Qu, W. 2015: Predicting cytotoxicity of complex mixtures in high cancer incidence regions of the Huai River Basin based on GC-MS spectrum with partial least squares regression. Environmental Research 137: 391-397

Wade, E.; Lin, P.; Hemmati, S.; Sigward, S. 2015: Predicting daily gait behaviors after anterior cruciate ligament surgery: a case study. Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference 2015: 6752-6755

Rand, D.; Eng, J.J. 2015: Predicting daily use of the affected upper extremity 1 year after stroke. Journal of Stroke and Cerebrovascular Diseases: the Official Journal of National Stroke Association 24(2): 274-283

Tran, V.-T.; Porcher, R.; Tran, V.-C.; Ravaud, P. 2017: Predicting data saturation in qualitative surveys with mathematical models from ecological research. Journal of Clinical Epidemiology 82: 71-78.E2

Xie, Y.; Schreier, G.; Chang, D.C.W.; Neubauer, S.; Liu, Y.; Redmond, S.J.; Lovell, N.H. 2015: Predicting days in hospital using health insurance claims. IEEE Journal of Biomedical and Health Informatics 19(4): 1224-1233

O'Dowd, E.L.; Lüchtenborg, M.; Baldwin, D.R.; McKeever, T.M.; Powell, H.A.; Møller, H.; Jakobsen, E.; Hubbard, R.B. 2016: Predicting death from surgery for lung cancer: a comparison of two scoring systems in two European countries. Lung Cancer 95: 88-93

Brar, R.; Tangri, N. 2015: Predicting death without dialysis in elderly patients with CKD. Clinical Journal of the American Society of Nephrology: Cjasn 10(3): 341-343

Creutzfeldt, C.J.; Longstreth, W.T.; Holloway, R.G. 2015: Predicting decline and survival in severe acute brain injury: the fourth trajectory. Bmj 351: H3904

Abulaban, K.M.; Song, H.; Zhang, X.; Kimmel, P.L.; Kusek, J.W.; Nelson, R.G.; Feldman, H.I.; Vasan, R.S.; Ying, J.; Mauer, M.; Nelsestuen, G.L.; Bennett, M.; Brunner, H.I.; Rovin, B.H. 2016: Predicting decline of kidney function in lupus nephritis using urine biomarkers. Lupus 25(9): 1012-1018

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Zhou, Z.; Folkert, M.; Cannon, N.; Iyengar, P.; Westover, K.; Zhang, Y.; Choy, H.; Timmerman, R.; Yan, J.; Xie, X.-J.; Jiang, S.; Wang, J. 2016: Predicting distant failure in early stage NSCLC treated with SBRT using clinical parameters. RadioTherapy and Oncology: Journal of the European Society for Therapeutic Radiology and Oncology 119(3): 501-504

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Gailani, J.Z.; Lackey, T.C.; King, D.B.; Bryant, D.; Kim, S.-C.; Shafer, D.J. 2016: Predicting dredging-associated effects to coral reefs in Apra Harbor, Guam - Part 1: Sediment exposure modeling. Journal of Environmental Management 168: 16-26

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Zhang, H.; Ding, L.; Zou, Y.; Hu, S.-Q.; Huang, H.-G.; Kong, W.-B.; Zhang, J. 2016: Predicting drug-induced liver injury in human with Naïve Bayes classifier approach. Journal of Computer-Aided Molecular Design 30(10): 889-898

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Duchnowska, R.; Jassem, J.; Goswami, C.P.; Dundar, M.; Gökmen-Polar, Y.; Li, L.; Woditschka, S.; Biernat, W.; Sosińska-Mielcarek, K.; Czartoryska-Arłukowicz, B.ła.; Radecka, B.; Tomasevic, Z.; Stępniak, P.; Wojdan, K.; Sledge, G.W.; Steeg, P.S.; Badve, S. 2015: Predicting early brain metastases based on clinicopathological factors and gene expression analysis in advanced HER2-positive breast cancer patients. Journal of Neuro-Oncology 122(1): 205-216

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Narita, M.; Oussoultzoglou, E.; Chenard, M-Pierre.; Fuchshuber, P.; Yamamoto, T.; Addeo, P.; Jaeck, D.; Bachellier, P. 2015: Predicting early intrahepatic recurrence after curative resection of colorectal liver metastases with molecular markers. World Journal of Surgery 39(5): 1167-1176

Sohal, D.P.S.; Shrotriya, S.; Glass, K.T.; Pelley, R.J.; McNamara, M.J.; Estfan, B.; Shapiro, M.; Wey, J.; Chalikonda, S.; Morris-Stiff, G.; Walsh, R.M.; Khorana, A.A. 2015: Predicting early mortality in resectable pancreatic adenocarcinoma: a cohort study. Cancer 121(11): 1779-1784

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Iwata, H.; Mori, E.; Tsuchiya, M.; Sakajo, A.; Maehara, K.; Ozawa, H.; Morita, A.; Maekawa, T.; Aoki, K.; Makaya, M.; Tamakoshi, K. 2015: Predicting early post-partum depressive symptoms among older primiparous Japanese mothers. Japan Journal of Nursing Science: Jjns 12(4): 297-308

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Kraft, I.; Schreiber, J.; Cafiero, R.; Metere, R.; Schaadt, G.; Brauer, J.; Neef, N.E.; Müller, B.; Kirsten, H.; Wilcke, A.; Boltze, J.; Friederici, A.D.; Skeide, M.A. 2016: Predicting early signs of dyslexia at a preliterate age by combining behavioral assessment with structural MRi. Neuroimage 143: 378-386

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Wilkey, J.; Kelly, K.; Jaramillo, I.Cristina.; Spinti, J.; Ring, T.; Hogue, M.; Pasqualini, D. 2016: Predicting emissions from oil and gas operations in the Uinta Basin, Utah. Journal of the Air and Waste Management Association 66(5): 528-545

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Farooqi, N.; Slinde, F.; Carlsson, M.; Håglin, L.; Sandström, T. 2015: Predicting energy requirement with pedometer-determined physical-activity level in women with chronic obstructive pulmonary disease. International Journal of Chronic Obstructive Pulmonary Disease 10: 1129-1137

Haller, M.; Myers, U.S.; McKnight, A.; Angkaw, A.C.; Norman, S.B. 2016: Predicting engagement in psychotherapy, pharmacotherapy, or both psychotherapy and pharmacotherapy among returning veterans seeking PTSD treatment. Psychological Services 13(4): 341-348

DeRolph, C.R.; Schramm, M.P.; Bevelhimer, M.S. 2016: Predicting environmental mitigation requirements for hydropower projects through the integration of biophysical and socio-political geographies. Science of the Total Environment 566-567: 888-918

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Russo, M.ía.J.; Cohen, G.; Chrem Mendez, P.; Campos, J.; Nahas, F.E.; Surace, E.I.; Vazquez, S.; Gustafson, D.; Guinjoan, S.; Allegri, R.F.; Sevlever, G. 2016: Predicting episodic memory performance using different biomarkers: results from Argentina-Alzheimer's Disease Neuroimaging Initiative. Neuropsychiatric Disease and Treatment 12: 2199-2206

Parinet, J.; Julien, M.; Nun, P.; Robins, R.J.; Remaud, G.; Höhener, P. 2015: Predicting equilibrium vapour pressure isotope effects by using artificial neural networks or multi-linear regression - A quantitative structure property relationship approach. Chemosphere 134: 521-527

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Zhang, F.; Wu, M.; Li, X.-J.; Li, X.-L.; Kwoh, C.K.; Zheng, J. 2015: Predicting essential genes and synthetic lethality via influence propagation in signaling pathways of cancer cell fates. Journal of Bioinformatics and Computational Biology 13(3): 1541002

Li, G.; Li, M.; Wang, J.; Wu, J.; Wu, F-Xiang.; Pan, Y. 2016: Predicting essential proteins based on subcellular localization, orthology and PPI networks. Bmc Bioinformatics 17 Suppl. 8: 279

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Nagamatsu, Y.; Sueyoshi, S.; Sasahara, H.; Oka, Y.; Kumazoe, H.; Mitsuoka, M.; Akagi, Y. 2016: Predicting exercise capacity after lobectomy by single photon emission computed tomography and computed tomography. General Thoracic and Cardiovascular Surgery 64(9): 537-542

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Stark, K.; Andersson, P.; Beresford, N.A.; Yankovich, T.L.; Wood, M.D.; Johansen, M.P.; Vives i Batlle, J.; Twining, J.; Keum, D.-K.; Bollhöfer, A.; Doering, C.; Ryan, B.; Grzechnik, M.; Vandenhove, H. 2015: Predicting exposure of wildlife in radionuclide contaminated wetland ecosystems. Environmental Pollution 196: 201-213

Madaniyazi, L.; Guo, Y.; Chen, R.; Kan, H.; Tong, S. 2016: Predicting exposure-response associations of ambient particulate matter with mortality in 73 Chinese cities. Environmental Pollution 208(Pt A): 40-47

Budden, D.M.; Hurley, D.G.; Cursons, J.; Markham, J.F.; Davis, M.J.; Crampin, E.J. 2014: Predicting expression: the complementary power of histone modification and transcription factor binding data. Epigenetics and Chromatin 7(1): 36

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Su, Z.; Duan, Z.; Pan, W.; Wu, C.; Jia, Y.; Han, B.; Li, C. 2016: Predicting extracapsular spread of head and neck cancers using different imaging techniques: a systematic review and meta-analysis. International Journal of Oral and Maxillofacial Surgery 45(4): 413-421

Cant, J.P.; Madsen, T.G.; Cieslar, S.R.L. 2016: Predicting extraction and uptake of arterial energy metabolites by the mammary glands of lactating cows when blood flow is perturbed. Journal of Dairy Science 99(1): 718-732

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Fagertun, J.; Wolffhechel, K.; Pers, T.H.; Nielsen, H.B.; Gudbjartsson, D.; Stefansson, H.; Stefansson, K.ár.; Paulsen, R.R.; Jarmer, H. 2015: Predicting facial characteristics from complex polygenic variations. Forensic Science International. Genetics 19: 263-268

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Ferrer, A.; Formiga, F.; Cunillera, O.; Megido, M.J.ús.; Corbella, X.; Almeda, J.ús.; Almeda, J.; Cunillera, O.; Fernández, C.; Gil, A.; Llopart, C.; Megido, M.J.; Padrós, G.; Sarró, M.; Tobella, A. 2015: Predicting factors of health-related quality of life in octogenarians: a 3-year follow-up longitudinal study. Quality of Life Research: An International Journal of Quality of Life Aspects of Treatment Care and Rehabilitation 24(11): 2701-2711

Bordier, L.; Buysschaert, M.; Bauduceau, B.; Doucet, J.; Verny, C.; Lassmann Vague, V.; Le Floch, J.P.; Bauduceau, B.; Blicklé, J.-F.; Bourdel-Marchasson, I.; Constans, T.; Doucet, J.; Fagot-Campagna, A.; Kaloustian, E.; Lassmann-Vague, V.; Lecomte, P.; Tessier, D.; Verny, C.; Vischer, U.; Affres, H.; Alix, M.; Archambeaud, F.; Barrou, Z.; Bauduceau, B.; Beau, P.; Beltran, S.; Benoit, C.; Beressi, J.-P.; Bernachon, F.; Berne, C.; Berrut, G.; Blaimont, A.; Blickle, J.-F.; Boda-Buccino, M.; Bohatier, J.; Böhme, P.; Bordier, L.; Bouchou, K.; Bouillet, B.; Bouilloud, F.; Bouix, R.; Boulanger, E.; Bourdel-Marchasson, I.; Bourgon, C.; Bourrinet, E.; Brocker, P.; Bruckert, I.; Capet, C.; Carette, C.; Cariou, B.; Carreau, A.; Vaurie, C.C.; Chamou 2015: Predicting factors of hypoglycaemia in elderly type 2 diabetes patients: Contributions of the GERODIAB study. Diabetes and Metabolism 41(4): 301-303

Mirbolouk, F.; Yousefnezhad, A.; Ghanbari, A. 2015: Predicting factors of medical treatment success with single dose methotrexate in tubal ectopic pregnancy: a retrospective study. Iranian Journal of Reproductive Medicine 13(6): 351-354

Ten Hoeve, Y.; Castelein, S.; Jansen, W.; Jansen, G.; Roodbol, P. 2016: Predicting factors of positive orientation and attitudes towards nursing: A quantitative cross-sectional study. Nurse Education Today 40: 111-117

Chaturvedi, S.; Campbell, B. 2016: Predicting failure of acute stroke intervention. Neurology 87(15): 1528-1529

Sano, H.; Imagawa, K.; Yamamoto, N.; Ozawa, H.; Yokobori, A.T.; Itoi, E. 2015: Predicting failures of suture anchors used for rotator cuff repair: a CT-based 3-dimensional finite element analysis. Bio-Medical Materials and Engineering 25(4): 371-380

Anonymous 2001: Predicting fallers in a communitybased sample of people with Parkinson's disease. Nursing Older People 13(8): 8

Lazkani, A.; Delespierre, T.; Bauduceau, B.; Benattar-Zibi, L.; Bertin, P.; Berrut, G.; Corruble, E.; Danchin, N.; Derumeaux, G.èv.; Doucet, J.; Falissard, B.; Forette, F.; Hanon, O.; Pasquier, F.; Pinget, M.; Ourabah, R.; Piedvache, C.; Becquemont, L. 2015: Predicting falls in elderly patients with chronic pain and other chronic conditions. Aging Clinical and Experimental Research 27(5): 653-661

Cardon-Verbecq, C.; Loustau, M.; Guitard, E.; Bonduelle, M.; Delahaye, E.; Koskas, P.; Raynaud-Simon, A. 2017: Predicting falls with the cognitive timed up-and-go dual task in frail older patients. Annals of Physical and Rehabilitation Medicine 60(2): 83-86

McKechnie, D.; Pryor, J.; Fisher, M.J. 2016: Predicting falls: considerations for screening tool selection vs. screening tool development. Journal of Advanced Nursing 72(9): 2238-2250

Menichini, C.; Cheng, Z.; Gibbs, R.G.J.; Xu, X.Yun. 2016: Predicting false lumen thrombosis in patient-specific models of aortic dissection. Journal of the Royal Society Interface 13(124)

Carabús, A.; Sainz, R.D.; Oltjen, J.W.; Gispert, M.; Font-i-Furnols, M. 2015: Predicting fat, lean and the weights of primal cuts for growing pigs of different genotypes and sexes using computed tomography. Journal of Animal Science 93(3): 1388-1397

Denkinger, M.D.; Hasch, M.; Gerstmayer, A.; Kreienberg, R.; Nikolaus, T.; Hancke, K. 2015: Predicting fatigue in older breast cancer patients receiving radiotherapy. a head-to-head comparison of established assessments. Zeitschrift für Gerontologie und Geriatrie 48(2): 128-134

Hallmann, J.; Kolossa, S.; Gedrich, K.; Celis-Morales, C.; Forster, H.; O'Donovan, C.B.; Woolhead, C.; Macready, A.L.; Fallaize, R.; Marsaux, C.F.M.; Lambrinou, C.-P.; Mavrogianni, C.; Moschonis, G.; Navas-Carretero, S.; San-Cristobal, R.; Godlewska, M.; Surwiłło, A.; Mathers, J.C.; Gibney, E.R.; Brennan, L.; Walsh, M.C.; Lovegrove, J.A.; Saris, W.H.M.; Manios, Y.; Martinez, J.A.; Traczyk, I.; Gibney, M.J.; Daniel, H. 2015: Predicting fatty acid profiles in blood based on food intake and the FADS1 rs174546 SNP. Molecular Nutrition and Food Research 59(12): 2565-2573

Swan, M.; Christie, F.; Sitters, H.; York, A.; Di Stefano, J. 2015: Predicting faunal fire responses in heterogeneous landscapes: the role of habitat structure. Ecological Applications: a Publication of the Ecological Society of America 25(8): 2293-2305

Pettus, P.; Foster, E.; Pan, Y. 2015: Predicting fecal indicator organism contamination in Oregon coastal streams. Environmental Pollution 207: 68-78

Casanovas-Massana, A.; Gómez-Doñate, M.; Sánchez, D.; Belanche-Muñoz, L.ís.A.; Muniesa, M.; Blanch, A.R. 2015: Predicting fecal sources in waters with diverse pollution loads using general and molecular host-specific indicators and applying machine learning methods. Journal of Environmental Management 151: 317-325

Bastianelli, D.; Bonnal, L.; Jaguelin-Peyraud, Y.; Noblet, J. 2015: Predicting feed digestibility from NIRS analysis of pig faeces. Animal: An International Journal of Animal Bioscience 9(5): 781-786

Lavo, J.Patrick.; Ludlow, D.; Morgan, M.; Caldito, G.; Nathan, C-Ann. 2017: Predicting feeding tube and tracheotomy dependence in laryngeal cancer patients. Acta Oto-Laryngologica 137(3): 326-330

Ross, M.G.; Amaya, K. 2015: Predicting fetal acidemia using umbilical venous cord gas parameters. Obstetrics and Gynecology 125(3): 741

Namburete, A.I.L.; Yaqub, M.; Kemp, B.; Papageorghiou, A.T.; Noble, J.A. 2014: Predicting fetal neurodevelopmental age from ultrasound images. Medical Image Computing and Computer-Assisted Intervention: Miccai . International Conference on Medical Image Computing and Computer-Assisted Intervention 17(Part 2): 260-267

DeRolph, C.R.; Nelson, S.A.C.; Kwak, T.J.; Hain, E.F. 2015: Predicting fine-scale distributions of peripheral aquatic species in headwater streams. Ecology and Evolution 5(1): 152-163

Lord, S.; Galna, B.; Yarnall, A.J.; Coleman, S.; Burn, D.; Rochester, L. 2016: Predicting first fall in newly diagnosed Parkinson's disease: Insights from a fall-naïve cohort. Movement Disorders: Official Journal of the Movement Disorder Society 31(12): 1829-1836

LeMoult, J.; Ordaz, S.J.; Kircanski, K.; Singh, M.K.; Gotlib, I.H. 2015: Predicting first onset of depression in young girls: Interaction of diurnal cortisol and negative life events. Journal of Abnormal Psychology 124(4): 850-859

Erickson, A.M.; Henry, B.I.; Murray, J.M.; Klasse, P.Johan.; Angstmann, C.N. 2015: Predicting first traversal times for virions and nanoparticles in mucus with slowed diffusion. Biophysical Journal 109(1): 164-172

Lu, C.-H.; Yu, C.-S.; Lin, Y.-F.; Chen, J.-Y. 2015: Predicting flavin and nicotinamide adenine dinucleotide-binding sites in proteins using the fragment transformation method. Biomed Research International 2015: 402536

Saxena, R.; Durward, A.; Steeley, S.; Murdoch, I.A.; Tibby, S.M. 2015: Predicting fluid responsiveness in 100 critically ill children: the effect of baseline contractility. Intensive Care Medicine 41(12): 2161-2169

Lane, S.H.; Hines, A.; Krowchuk, H. 2015: Predicting folic acid intake among college students. MCN. American journal of maternal child nursing 40(1): 51-57

Lundorff, P.; Brölmann, H.; Koninckx, P.R.; Mara, M.; Wattiez, A.; Wallwiener, M.; Trew, G.; Crowe, A.M.; De Wilde, R.L. 2015: Predicting formation of adhesions after gynaecological surgery: development of a risk score. Archives of Gynecology and Obstetrics 292(4): 931-938

Aw, D.; Thain, J.; Ali, A.; Aung, T.; Chua, W.M.; Sahota, O.; Weerasuriya, N.; Marshall, L.; Kearney, F.; Masud, T. 2016: Predicting fracture risk in osteoporosis: the use of fracture prediction tools in an osteoporosis clinic population. Postgraduate Medical Journal 92(1087): 267-270

Rollemberg, C.V.V.; Silva, M.íl.M.B.L.; Rollemberg, K.C.; Amorim, F.áb.R.; Lessa, N.M.N.; Santos, M.D.S.; Souza, A.ác.M.B.; Melo, E.V.; Almeida, R.P.; Silva, Ân.M.; Werneck, G.L.; Santos, M.A.; Almeida, J.é A.P.; Jesus, A.él.R. 2015: Predicting frequency distribution and influence of sociodemographic and behavioral risk factors of Schistosoma mansoni infection and analysis of co-infection with intestinal parasites. Geospatial Health 10(1): 303

Kerkhof, M.; Freeman, D.; Jones, R.; Chisholm, A.; Price, D.B. 2015: Predicting frequent COPD exacerbations using primary care data. International Journal of Chronic Obstructive Pulmonary Disease 10: 2439-2450

Grinspan, Z.M.; Shapiro, J.S.; Abramson, E.L.; Hooker, G.; Kaushal, R.; Kern, L.M. 2015: Predicting frequent ED use by people with epilepsy with health information exchange data. Neurology 85(12): 1031-1038

Price, D.; Wilson, A.M.; Chisholm, A.; Rigazio, A.; Burden, A.; Thomas, M.; King, C. 2016: Predicting frequent asthma exacerbations using blood eosinophil count and other patient data routinely available in clinical practice. Journal of Asthma and Allergy 9: 1-12

Low, L.Leng.; Liu, N.; Wang, S.; Thumboo, J.; Ong, M.Eng.Hock.; Lee, K.Hock. 2016: Predicting frequent hospital admission risk in Singapore: a retrospective cohort study to investigate the impact of comorbidities, acute illness burden and social determinants of health. Bmj Open 6(10): E012705

Siwo, G.H.; Tan, A.; Button-Simons, K.A.; Samarakoon, U.; Checkley, L.A.; Pinapati, R.S.; Ferdig, M.T. 2015: Predicting functional and regulatory divergence of a drug resistance transporter gene in the human malaria parasite. Bmc Genomics 16: 115

Messé, A.; Rudrauf, D.; Giron, A.; Marrelec, G. 2015: Predicting functional connectivity from structural connectivity via computational models using MRI: an extensive comparison study. Neuroimage 111: 65-75

Lowthian, J.A.; Straney, L.D.; Brand, C.A.; Barker, A.; Smit, P.d.V.; Newnham, H.; Hunter, P.; Smith, C.; Cameron, P.A. 2017: Predicting functional decline in older emergency patients-the Safe Elderly Emergency Discharge (SEED) project. Age and Ageing 46(2): 219-225

Ferroli, P.; Broggi, M.; Schiavolin, S.; Acerbi, F.; Bettamio, V.; Caldiroli, D.; Cusin, A.; La Corte, E.; Leonardi, M.; Raggi, A.; Schiariti, M.; Visintini, S.; Franzini, A.; Broggi, G. 2015: Predicting functional impairment in brain tumor surgery: the Big Five and the Milan Complexity Scale. Neurosurgical Focus 39(6): E14

Ntaios, G.; Papavasileiou, V.; Michel, P.; Tatlisumak, T.; Strbian, D. 2015: Predicting functional outcome and symptomatic intracranial hemorrhage in patients with acute ischemic stroke: a glimpse into the crystal ball?. Stroke 46(3): 899-908

Flores Saiffe Farías, A.; Jaime Herrera López, E.; Moreno Vázquez, C.J.; Li, W.; Prado Montes de Oca, E. 2015: Predicting functional regulatory SNPs in the human antimicrobial peptide genes DEFB1 and CAMP in tuberculosis and HIV/AIDS. Computational Biology and Chemistry 59 Pt A: 117-125

Valencia, M.; Fresán, A.; Barak, Y.; Juárez, F.; Escamilla, R.; Saracco, R. 2015: Predicting functional remission in patients with schizophrenia: a cross-sectional study of symptomatic remission, psychosocial remission, functioning, and clinical outcome. Neuropsychiatric Disease and Treatment 11: 2339-2348

Akita, T.; Tanaka, J.; Ohisa, M.; Sugiyama, A.; Nishida, K.; Inoue, S.; Shirasaka, T. 2016: Predicting future blood supply and demand in Japan with a Markov model: application to the sex- and age-specific probability of blood donation. Transfusion 56(11): 2750-2759

Danese, S.; Peyrin-Biroulet, L.; Fiorino, G. 2015: Predicting future disease course in Crohn's disease by colonoscopy or magnetic resonance: which is the crystal ball?. Gut 64(9): 1347-1348

Wu, D.J.; Hipolito, E.; Bilderback, A.; Okelo, S.O.; Garro, A. 2016: Predicting future emergency department visits and hospitalizations for asthma using the Pediatric Asthma Control and Communication Instrument - Emergency Department version (PACCI-ED). Journal of asthma: official journal of the Association for the Care of Asthma 53(4): 387-391

Szczesniak, R.D.; McPhail, G.L.; Li, D.; Amin, R.S.; Clancy, J.P. 2016: Predicting future lung function decline in cystic fibrosis patients: Statistical methods and clinical connections. Pediatric Pulmonology 51(2): 217-218

Cook, C. 2016: Predicting future physical injury in sports: it's a complicated dynamic system. British Journal of Sports Medicine 50(22): 1356-1357

Hu, C.; Harber, P.; Su, J. 2016: Predicting future protection of respirator users: Statistical approaches and practical implications. Journal of Occupational and Environmental Hygiene 13(5): 393-400

Marras, S.; Cucco, A.; Antognarelli, F.; Azzurro, E.; Milazzo, M.; Bariche, M.; Butenschön, M.; Kay, S.; Di Bitetto, M.; Quattrocchi, G.; Sinerchia, M.; Domenici, P. 2015: Predicting future thermal habitat suitability of competing native and invasive fish species: from metabolic scope to oceanographic modelling. Conservation Physiology 3(1): Cou059

Van den Biggelaar, A.H.J.; Poolman, J.T. 2016: Predicting future trends in the burden of pertussis in the 21st century: implications for infant pertussis and the success of maternal immunization. Expert Review of Vaccines 15(1): 69-80

Li, A.; Gao, J.; Freels, S.; Huang, J.; Yu, G. 2016: Predicting gas chromatography relative retention times for polychlorinated biphenyls using chlorine substitution pattern contribution method. Journal of Chromatography. a 1427: 161-169

Fournel, S.; Marcos, B.; Godbout, S.; Heitz, M. 2015: Predicting gaseous emissions from small-scale combustion of agricultural biomass fuels. Bioresource Technology 179: 165-172

Lehto, E.; Ray, C.; Haukkala, A.; Yngve, A.; Thorsdottir, I.; Roos, E. 2015: Predicting gender differences in liking for vegetables and preference for a variety of vegetables among 11-year-old children. Appetite 95: 285-292

Zhang, L.-Q.; Li, Q.-Z.; Su, W.-X.; Jin, W. 2016: Predicting gene expression level by the transcription factor binding signals in human embryonic stem cells. Bio Systems 150: 92-98

Reddy, C.K.; Aziz, M.S. 2015: Predicting gene functions from multiple biological sources using novel ensemble methods. International Journal of Data Mining and Bioinformatics 12(2): 184-206

Belinsky, S.A.; Grimes, M.; Johnson, D.; Levy, D.; Schiller, J. 2016: Predicting gene promoter methylation in lung tumors through examination of sputum and serum. Journal of Clinical Oncology 24(18_Suppl): 7208-7208

Pavlides, J.M.Whitehead.; Zhu, Z.; Gratten, J.; McRae, A.F.; Wray, N.R.; Yang, J. 2016: Predicting gene targets from integrative analyses of summary data from GWAS and eQTL studies for 28 human complex traits. Genome Medicine 8(1): 84

Gibbons, A.; Groarke, A.; Sweeney, K. 2016: Predicting general and cancer-related distress in women with newly diagnosed breast cancer. Bmc Cancer 16(1): 935

Pflueger, M.O.; Franke, I.; Graf, M.; Hachtel, H. 2015: Predicting general criminal recidivism in mentally disordered offenders using a random forest approach. Bmc Psychiatry 15: 62

Ngune, I.; Jiwa, M.; McManus, A.; Parsons, R.; Hodder, R. 2015: Predicting general practice attendance for follow-up cancer care. American Journal of Health Behavior 39(2): 167-174

Ruckerbauer, D.E.; Jungreuthmayer, C.; Zanghellini, J.ür. 2015: Predicting genetic engineering targets with Elementary Flux Mode Analysis: a review of four current methods. New Biotechnology 32(6): 534-546

Calzone, L.; Barillot, E.; Zinovyev, A. 2015: Predicting genetic interactions from Boolean models of biological networks. Integrative Biology: Quantitative Biosciences from Nano to Macro 7(8): 921-929

Li, P.; Ma, H.; Zhao, X.; Chen, T. 2016: Predicting genetic modification targets based on metabolic network analysis--a review. Sheng Wu Gong Cheng Xue Bao 32(1): 1-13

Bagalà, P.; Ingrosso, G.; Falco, M.D.; Petrichella, S.; D'Andrea, M.; Rago, M.; Lancia, A.; Bruni, C.; Ponti, E.; Santoni, R. 2016: Predicting genitourinary toxicity in three-dimensional conformal radiotherapy for localized prostate cancer: a dose-volume parameters analysis of the bladder. Journal of Cancer Research and Therapeutics 12(2): 1018-1024

Suzuki, Y.; Doan, Y.H.; Kimura, H.; Shinomiya, H.; Shirabe, K.; Katayama, K. 2016: Predicting genotype compositions in norovirus seasons in Japan. Microbiology and Immunology 60(6): 418-426

Ryckman, K.K.; Berberich, S.L.; Dagle, J.M. 2016: Predicting gestational age using neonatal metabolic markers. American Journal of Obstetrics and Gynecology 214(4): 515.E1-515.E13

Alptekin, H.üs.ü; Çizmecioğlu, A.; Işık, H.; Cengiz, T.ür.; Yildiz, M.; Iyisoy, M.S. 2016: Predicting gestational diabetes mellitus during the first trimester using anthropometric measurements and HOMA-IR. Journal of Endocrinological Investigation 39(5): 577-583

Kessler, T. 2016: Predicting glioblastoma response to bevacizumab through marker profiling?. Neuro-Oncology 18(2): 149-150

Barabás, G.ör.; Allesina, S. 2015: Predicting global community properties from uncertain estimates of interaction strengths. Journal of the Royal Society Interface 12(109): 20150218

Jensen, P.M.; De Fine Licht, H.H. 2016: Predicting global variation in infectious disease severity: a bottom-up approach. Evolution Medicine and Public Health 1: 85-94

Schumacher, K.R.; Almond, C.; Singh, T.P.; Kirk, R.; Spicer, R.; Hoffman, T.M.; Hsu, D.; Naftel, D.C.; Pruitt, E.; Zamberlan, M.; Canter, C.E.; Gajarski, R.J. 2015: Predicting graft loss by 1 year in pediatric heart transplantation candidates: an analysis of the Pediatric Heart Transplant Study database. Circulation 131(10): 890-898

Osis, S.T.; Hettinga, B.A.; Ferber, R. 2016: Predicting ground contact events for a continuum of gait types: An application of targeted machine learning using principal component analysis. Gait and Posture 46: 86-90

Close, M.E.; Abraham, P.; Humphries, B.; Lilburne, L.; Cuthill, T.; Wilson, S. 2016: Predicting groundwater redox status on a regional scale using linear discriminant analysis. Journal of Contaminant Hydrology 191: 19-32

Bassim, S.; Chapman, R.W.; Tanguy, A.; Moraga, D.; Tremblay, R. 2015: Predicting growth and mortality of bivalve larvae using gene expression and supervised machine learning. Comparative Biochemistry and Physiology. Part D Genomics and Proteomics 16: 59-72

Sridhara, V.; Meyer, A.G.; Rai, P.; Barrick, J.E.; Ravikumar, P.; Segrè, D.; Wilke, C.O. 2014: Predicting growth conditions from internal metabolic fluxes in an in-silico model of E. coli. Plos one 9(12): E114608

Baranowski, T.; Chen, T-An.; O'Connor, T.M.; Hughes, S.O.; Diep, C.S.; Beltran, A.; Brand, L.; Nicklas, T.; Baranowski, J. 2016: Predicting habits of vegetable parenting practices to facilitate the design of change programmes. Public Health Nutrition 19(11): 1976-1982

Tewarie, P.; Bright, M.G.; Hillebrand, A.; Robson, S.E.; Gascoyne, L.E.; Morris, P.G.; Meier, J.; Van Mieghem, P.; Brookes, M.J. 2016: Predicting haemodynamic networks using electrophysiology: the role of non-linear and cross-frequency interactions. Neuroimage 130: 273-292

Van Mourik, S.; Alders, B.P.G.J.; Helderman, F.; van de Ven, L.J.F.; Groot Koerkamp, P.W.G. 2017: Predicting hairline fractures in eggs of mature hens. Poultry Science 96(6): 1956-1962

Paek, A.Y.; Gailey, A.; Parikh, P.; Santello, M.; Contreras-Vidal, J. 2015: Predicting hand forces from scalp electroencephalography during isometric force production and object grasping. Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference 2015: 7570-7573

Küpper, H.; Kudernatsch, M.; Pieper, T.; Groeschel, S.; Tournier, J.-D.; Raffelt, D.; Winkler, P.; Holthausen, H.; Staudt, M. 2016: Predicting hand function after hemidisconnection. Brain: a Journal of Neurology 139(Part 9): 2456-2468

Liu, C.-J.; Marie, D.; Fredrick, A.; Bertram, J.; Utley, K.; Fess, E.E. 2017: Predicting hand function in older adults: evaluations of grip strength, arm curl strength, and manual dexterity. Aging Clinical and Experimental Research 29(4): 753-760

Zhang, P.; Ma, X.; Huang, H.; He, J. 2014: Predicting hand orientation in reach-to-grasp tasks using neural activities from primary motor cortex. Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference 2014: 1306-1309

Biffani, S.; Dimauro, C.; Macciotta, N.ò; Rossoni, A.; Stella, A.; Biscarini, F. 2015: Predicting haplotype carriers from SNP genotypes in Bos taurus through linear discriminant analysis. Genetics Selection Evolution: Gse 47: 4

Chen, M.-F.; Wang, R.-H.; Hung, S.-L. 2015: Predicting health-promoting self-care behaviors in people with pre-diabetes by applying Bandura social learning theory. Applied Nursing Research: Anr 28(4): 299-304

Ferriz, R.; González-Cutre, D.; Sicilia, Á; Hagger, M.S. 2016: Predicting healthy and unhealthy behaviors through physical education: a self-determination theory-based longitudinal approach. Scandinavian Journal of Medicine and Science in Sports 26(5): 579-592

Lin, L.; Jin, C.; Fu, Z.; Zhang, B.; Bin, G.; Wu, S. 2016: Predicting healthy older adult's brain age based on structural connectivity networks using artificial neural networks. Computer Methods and Programs in Biomedicine 125: 8-17

Hawkins, N.M.; Virani, S.A.; Sperrin, M.; Buchan, I.E.; McMurray, J.J.V.; Krahn, A.D. 2016: Predicting heart failure decompensation using cardiac implantable electronic devices: a review of practices and challenges. European Journal of Heart Failure 18(8): 977-986

Hu, Q.; Xiao, Z.; Xiong, X.; Zhou, G.; Guan, X. 2015: Predicting heavy metals' adsorption edges and adsorption isotherms on MnO2 with the parameters determined from Langmuir kinetics. Journal of Environmental Sciences 27: 207-216

Shahdoust, M.; Sadeghifar, M.; Poorolajal, J.; Javanrooh, N.; Amini, P. 2015: Predicting hepatitis B monthly incidence rates using weighted Markov chains and time series methods. Journal of Research in Health Sciences 15(1): 28-31

Liu, J.; Mansouri, K.; Judson, R.S.; Martin, M.T.; Hong, H.; Chen, M.; Xu, X.; Thomas, R.S.; Shah, I. 2015: Predicting hepatotoxicity using ToxCast in vitro bioactivity and chemical structure. Chemical Research in Toxicology 28(4): 738-751

Amuzu-Aweh, E.N.; Bovenhuis, H.; de Koning, D.-J.; Bijma, P. 2015: Predicting heterosis for egg production traits in crossbred offspring of individual White Leghorn sires using genome-wide SNP data. Genetics Selection Evolution: Gse 47: 27

Matharu, G.S.; Berryman, F.; Brash, L.; Pynsent, P.B.; Treacy, R.B.C.; Dunlop, D.J. 2015: Predicting high blood metal ion concentrations following hip resurfacing. Hip International: the Journal of Clinical and Experimental Research on Hip Pathology and Therapy 25(6): 510-519

Fischer, R.; Dreisbach, G. 2015: Predicting high levels of multitasking reduces between-tasks interactions. Journal of Experimental Psychology. Human Perception and Performance 41(6): 1482-1487

Leininger, L.J.; Saloner, B.; Wherry, L.R. 2015: Predicting high-cost pediatric patients: derivation and validation of a population-based model. Medical Care 53(8): 729-735

Bryant, R.J.; Sjoberg, D.D.; Vickers, A.J.; Robinson, M.C.; Kumar, R.; Marsden, L.; Davis, M.; Scardino, P.T.; Donovan, J.; Neal, D.E.; Lilja, H.; Hamdy, F.C. 2015: Predicting high-grade cancer at ten-core prostate biopsy using four kallikrein markers measured in blood in the ProtecT study. Journal of the National Cancer Institute 107(7)

Kaplan, N.M. 2016: Predicting home-clinic blood pressure differences. Journal of the American Society of Hypertension: Jash 10(3): 186

Ireland, J.L.; Priday, L.J.; Ireland, C.A.; Chu, S.; Kilcoyne, J.; Mulligan, C. 2016: Predicting hospital aggression in secure psychiatric care. Bjpsych Open 2(1): 96-100

Li, B.; Cairns, J.; Fotheringham, J.; Ravanan, R. 2016: Predicting hospital costs for patients receiving renal replacement therapy to inform an economic evaluation. European Journal of Health Economics: Hepac: Health Economics in Prevention and Care 17(6): 659-668

Agarwal, V.; Han, L.; Madan, I.; Saluja, S.; Shidham, A.; Shah, N.H. 2016: Predicting hospital visits from geo-tagged Internet search logs. AMIA Joint Summits on Translational Science Proceedings. AMIA Joint Summits on Translational Science 2016: 15-24

Uszko-Lencer, N.H.M.K.; Frankenstein, L.; Spruit, M.A.; Maeder, M.T.; Gutmann, M.; Muzzarelli, S.; Osswald, S.; Pfisterer, M.E.; Zugck, C.; Brunner-La Rocca, H.-P. 2017: Predicting hospitalization and mortality in patients with heart failure: the BARDICHE-index. International Journal of Cardiology 227: 901-907

Chuang, W.-C.; Gober, P. 2015: Predicting hospitalization for heat-related illness at the census-tract level: accuracy of a generic heat vulnerability index in Phoenix, Arizona (USA). Environmental Health Perspectives 123(6): 606-612

Eng, C.L.P.; Tong, J.C.; Tan, T.W. 2014: Predicting host tropism of influenza a virus proteins using random forest. Bmc Medical Genomics 7(Suppl 3): S1

Xia, J.; Yue, Z.; Di, Y.; Zhu, X.; Zheng, C.-H. 2016: Predicting hot spots in protein interfaces based on protrusion index, pseudo hydrophobicity and electron-ion interaction pseudopotential features. Oncotarget 7(14): 18065-18075

Bailey, J.; Thew, M.; Balls, M. 2015: Predicting human drug toxicity and safety via animal tests: can any one species predict drug toxicity in any other, and do monkeys help?. Alternatives to Laboratory Animals: Atla 43(6): 393-403

Chowdhury, S. 2015: Predicting human exposure and risk from chlorinated indoor swimming pool: a case study. Environmental Monitoring and Assessment 187(8): 502

Lu, X.; Megchelenbrink, W.; Notebaart, R.A.; Huynen, M.A. 2015: Predicting human genetic interactions from cancer genome evolution. Plos one 10(5): E0125795

Waters, L.J.; Shokry, D.S.; Parkes, G.M.B. 2016: Predicting human intestinal absorption in the presence of bile salt with micellar liquid chromatography. Biomedical Chromatography: Bmc 30(10): 1618-1624

Basant, N.; Gupta, S.; Singh, K.P. 2016: Predicting human intestinal absorption of diverse chemicals using ensemble learning based QSAR modeling approaches. Computational Biology and Chemistry 61: 178-196

Li, J.; Fu, J.; Huang, X.; Lu, D.; Wu, J. 2016: Predicting hydration free energies of amphetamine-type stimulants with a customized molecular model. Journal of Physics. Condensed Matter: An Institute of Physics Journal 28(34): 344001

Wedler, H.B.; Palazzo, T.A.; Pemberton, R.P.; Hamann, C.S.; Kurth, M.J.; Tantillo, D.J. 2015: Predicting hydration propensities of biologically relevant α-ketoamides. Bioorganic and Medicinal Chemistry Letters 25(19): 4153-4157

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Bonafede, M.; Shi, N.; Barron, R.; Li, X.; Crittenden, D.B.; Chandler, D. 2016: Predicting imminent risk for fracture in patients aged 50 or older with osteoporosis using US claims data. Archives of Osteoporosis 11(1): 26

Pope, J.E. 2016: Predicting improvement in diffuse scleroderma: lessons learnt. Annals of the Rheumatic Diseases 75(10): 1741-1742

Kim, S.; Katchooi, M.; Bayiri, B.; Sarikaya, M.; Korpak, A.M.; Huang, G.J. 2016: Predicting improvement of postorthodontic white spot lesions. American Journal of Orthodontics and Dentofacial Orthopedics: Official Publication of the American Association of Orthodontists its Constituent Societies and the American Board of Orthodontics 149(5): 625-633

Dong, R.; Zhao, G. 2014: Predicting in vitro rumen VFA production using CNCPS carbohydrate fractions with multiple linear models and artificial neural networks. Plos one 9(12): E116290

Hormuth, D.A.; Weis, J.A.; Barnes, S.L.; Miga, M.I.; Rericha, E.C.; Quaranta, V.; Yankeelov, T.E. 2015: Predicting in vivo glioma growth with the reaction diffusion equation constrained by quantitative magnetic resonance imaging data. Physical Biology 12(4): 046006

Bauch, C.; Bevan, S.; Woodhouse, H.; Dilworth, C.; Walker, P. 2015: Predicting in vivo phospholipidosis-inducing potential of drugs by a combined high content screening and in silico modelling approach. Toxicology in Vitro: An International Journal Published in Association with Bibra 29(3): 621-630

Bakhshayesh, B.; Hosseininezhad, M.; Seyed Saadat, S.M.; Hajmanuchehri, M.; Kazemnezhad, E.; Ghayeghran, A.-R. 2014: Predicting in-hospital mortality in Iranian patients with spontaneous intracerebral hemorrhage. Iranian Journal of Neurology 13(4): 231-236

Van Belleghem, G.; Devos, S.; De Wit, L.; Hubloue, I.; Lauwaert, D.; Pien, K.; Putman, K. 2016: Predicting in-hospital mortality of traffic victims: A comparison between AIS-and ICD-9-CM-related injury severity scales when only ICD-9-CM is reported. Injury 47(1): 141-146

Allen, L.A.; McIlvennan, C.K. 2016: Predicting in-hospital worsening heart failure at time of admission, but do we really need another heart failure risk model?. American Heart Journal 178: 188-189

Dik, V.K.; Moons, L.M.G.; Hüyük, M.; van der Schaar, P.; de Vos Tot Nederveen Cappel, W.H.; Ter Borg, P.C.J.; Meijssen, M.A.C.; Ouwendijk, R.J.T.H.; Le Fèvre, D.M.; Stouten, M.; van der Galiën, O.; Hiemstra, T.J.; Monkelbaan, J.F.; van Oijen, M.G.H.; Siersema, P.D.; Moons, L.M.G.; van der Schaar, P.; de Vos Tot Nederveen Cappel, W.H.; Ter Borg, P.C.J.; Tang, T.J.; Ter Borg, F.; Meijssen, M.A.C.; Ouwendijk, R.J.T.H.; Le Fèvre, D.M.; Stouten, M.; van der Galiën, O.; Hiemstra, T.J.; Kuipers, E.J.; Siersema, P.D. 2015: Predicting inadequate bowel preparation for colonoscopy in participants receiving split-dose bowel preparation: development and validation of a prediction score. Gastrointestinal Endoscopy 81(3): 665-672

Basta, M.N.; Mirzabeigi, M.N.; Shubinets, V.; Kelz, R.R.; Williams, N.N.; Fischer, J.P. 2016: Predicting incisional hernia after bariatric surgery: a risk stratification model based upon 2161 operations. Surgery for Obesity and Related Diseases: Official Journal of the American Society for Bariatric Surgery 12(8): 1466-1473

Qin, J.; Chen, S.-G.; Hu, D.; Zeng, L.-L.; Fan, Y.-M.; Chen, X.-P.; Shen, H. 2015: Predicting individual brain maturity using dynamic functional connectivity. Frontiers in Human Neuroscience 9: 418

Wise, E.A.; Streiner, D.L.; Gallop, R.J. 2016: Predicting individual change during the course of treatment. PsychoTherapy Research: Journal of the Society for PsychoTherapy Research 26(5): 623-631

Puhan, M.A. 2016: Predicting individual lung-function trajectories: An opportunity for prevention?. Cmaj: Canadian Medical Association Journal 188(14): 997-998

Meng, X.; Jiang, R.; Lin, D.; Bustillo, J.; Jones, T.; Chen, J.; Yu, Q.; Du, Y.; Zhang, Y.; Jiang, T.; Sui, J.; Calhoun, V.D. 2017: Predicting individualized clinical measures by a generalized prediction framework and multimodal fusion of MRi data. Neuroimage 145(Part B): 218-229

Rekik, I.; Li, G.; Lin, W.; Shen, D. 2016: Predicting infant cortical surface development using a 4D varifold-based learning framework and local topography-based shape morphing. Medical Image Analysis 28: 1-12

Meira, K.ás.R.S.õe.; de Mattos Brito, C.S.; de Sousa, F.B. 2015: Predicting infiltration of the surface layer of natural enamel caries. Archives of Oral Biology 60(6): 883-893

Moniz, L.; Buczak, A.L.; Baugher, B.; Guven, E.; Chretien, J.-P. 2016: Predicting influenza with dynamical methods. Bmc Medical Informatics and Decision Making 16(1): 134

Bousardt, A.M.C.; Hoogendoorn, A.W.; Noorthoorn, E.O.; Hummelen, J.W.; Nijman, H.L.I. 2016: Predicting inpatient aggression by self-reported impulsivity in forensic psychiatric patients. Criminal Behaviour and Mental Health: Cbmh 26(3): 161-173

Chen, J.H.; Goldstein, M.K.; Asch, S.M.; Mackey, L.; Altman, R.B. 2017: Predicting inpatient clinical order patterns with probabilistic topic models vs conventional order sets. Journal of the American Medical Informatics Association: JAMIA 24(3): 472-480

Smith, M.W.; Friedman, B.; Karaca, Z.; Wong, H.S. 2015: Predicting inpatient hospital payments in the United States: a retrospective analysis. Bmc Health Services Research 15: 372

Liang, M.I.; Rosen, M.A.; Rath, K.S.; Hade, E.M.; Clements, A.E.; Backes, F.J.; Eisenhauer, E.L.; Salani, R.; O'Malley, D.M.; Fowler, J.M.; Cohn, D.E. 2015: Predicting inpatient stay lasting 2 midnights or longer after robotic surgery for endometrial cancer. Journal of Minimally Invasive Gynecology 22(4): 583-589

Fisher, C.; Karalapillai, D.K.; Bailey, M.; Glassford, N.G.; Bellomo, R.; Jones, D. 2015: Predicting intensive care and hospital outcome with the Dalhousie Clinical Frailty Scale: a pilot assessment. Anaesthesia and Intensive Care 43(3): 361-368

Sainsbury, K.; Mullan, B.; Sharpe, L. 2015: Predicting intention and behaviour following participation in a theory-based intervention to improve gluten free diet adherence in coeliac disease. Psychology and Health 30(9): 1063-1074

Akbar, H.; Anderson, D.; Gallegos, D. 2015: Predicting intentions and behaviours in populations with or at-risk of diabetes: a systematic review. Preventive Medicine Reports 2: 270-282

Chung, M.-H.; Ho, C.-H.; Wen, H.-C. 2016: Predicting intentions of nurses to adopt patient personal health records: a structural equation modeling approach. Computer Methods and Programs in Biomedicine 136: 45-53

Sun, H.; Wang, Y.; Zhang, Z.; Liu, L.; Yang, P. 2015: Predicting interatrial septum rotation: is the position of the heart or the direction of the coronary sinus reliable?: Implications for interventional electrophysiologists from CT studies. Pacing and clinical electrophysiology: PACE 38(4): 514-519

Schultz, A.é; Qutub, A.A. 2015: Predicting internal cell fluxes at sub-optimal growth. Bmc Systems Biology 9: 18

Sabdia, S.; Greer, R.M.; Prior, T.; Kumar, S. 2015: Predicting intrapartum fetal compromise using the fetal cerebro-umbilical ratio. Placenta 36(5): 594-598

Fan, L.; Lin, B.; Xu, T.; Xia, N.; Shao, X.; Tan, X.; Zhong, M.; Yang, Y.; Zhao, B. 2017: Predicting intraprocedural rupture and thrombus formation during coiling of ruptured anterior communicating artery aneurysms. Journal of Neurointerventional Surgery 9(4): 370-375

Qin, Z.; Zhang, J.-e.; DiTommaso, A.; Wang, R.-l.; Wu, R.-s. 2015: Predicting invasions of Wedelia trilobata (L.) Hitchc. with Maxent and GARP models. Journal of Plant Research 128(5): 763-775

Shahin, J.; Allen, E.J.; Patel, K.; Muskett, H.; Harvey, S.E.; Edgeworth, J.; Kibbler, C.C.; Barnes, R.A.; Biswas, S.; Soni, N.; Rowan, K.M.; Harrison, D.A. 2016: Predicting invasive fungal disease due to Candida species in non-neutropenic, critically ill, adult patients in United Kingdom critical care units. Bmc Infectious Diseases 16: 480

Yaqubi, M.; Mohammadnia, A.; Fallahi, H. 2015: Predicting involvement of polycomb repressive complex 2 in direct conversion of mouse fibroblasts into induced neural stem cells. Stem Cell Research and Therapy 6: 42

Mandell, B.F. 2016: Predicting is tough, especially about the future. Cleveland Clinic Journal of Medicine 83(5): 324

Valuckiene, Z.; Budrys, P.; Jurkevicius, R. 2016: Predicting ischemic mitral regurgitation in patients with acute ST-elevation myocardial infarction: Does time to reperfusion really matter and what is the role of collateral circulation?. International Journal of Cardiology 203: 667-671

Raskevicius, V.; Kairys, V. 2017: Predicting Isoform-specific Binding Selectivities of Benzensulfonamides Using QSAR and 3D-QSAR. Current Computer-Aided Drug Design 13(1): 75-83

Wang, F.; Dörfler, U.; Jiang, X.; Schroll, R. 2016: Predicting isoproturon long-term mineralization from short-term experiment: can this be a suitable approach?. Chemosphere 144: 312-318

Schmitt, N.; Pulakos, E.D. 1985: Predicting job satisfaction from life satisfaction: is there a general satisfaction factor?. International Journal of Psychology: Journal International de Psychologie 20(2): 155-167

Kirino, Y.; Hama, M.; Takase-Minegishi, K.; Kunishita, Y.; Kishimoto, D.; Yoshimi, R.; Asami, Y.; Ihata, A.; Oba, M.S.; Tsunoda, S.; Ohno, S.; Ueda, A.; Takeno, M.; Ishigatsubo, Y. 2015: Predicting joint destruction in rheumatoid arthritis with power Doppler, anti-citrullinated peptide antibody, and joint swelling. Modern Rheumatology 25(6): 842-848

Balankura, T.; Qi, X.; Zhou, Y.; Fichthorn, K.A. 2016: Predicting kinetic nanocrystal shapes through multi-scale theory and simulation: Polyvinylpyrrolidone-mediated growth of Ag nanocrystals. Journal of Chemical Physics 145(14): 144106

Waring, R.H.; Coops, N.C. 2016: Predicting large wildfires across western North America by modeling seasonal variation in soil water balance. Climatic Change 135: 325-339

Choi, J.; Park, S.W.; Kim, J.; Park, J.; Kim, J.S. 2016: Predicting late enophthalmos: Differences between medial and inferior orbital wall fractures. Journal of Plastic Reconstructive and Aesthetic Surgery: Jpras 69(12): E238-E244

Hamad, R.; Rehkopf, D.H.; Kuan, K.Y.; Cullen, M.R. 2016: Predicting later life health status and mortality using state-level socioeconomic characteristics in early life. Ssm - Population Health 2: 269-276

Zohsel, K.; Baldus, C.; Schmidt, M.H.; Esser, G.ün.; Banaschewski, T.; Thomasius, R.; Laucht, M. 2016: Predicting later problematic cannabis use from psychopathological symptoms during childhood and adolescence: Results of a 25-year longitudinal study. Drug and Alcohol Dependence 163: 251-255

Bazzi, W.M.; Sjoberg, D.D.; Grasso, A.A.C.; Bernstein, M.; Parra, R.; Coleman, J.A. 2015: Predicting length of stay after robotic partial nephrectomy. International Urology and Nephrology 47(8): 1321-1325

Paul Wright, G.; Stilwell, K.; Johnson, J.; Hefty, M.T.; Chung, M.H. 2015: Predicting length of stay and conversion to open cholecystectomy for acute cholecystitis using the 2013 Tokyo Guidelines in a US population. Journal of Hepato-Biliary-Pancreatic Sciences 22(11): 795-801

Van der Hoop, J.M.; Corkeron, P.; Henry, A.G.; Knowlton, A.R.; Moore, M.J. 2017: Predicting lethal entanglements as a consequence of drag from fishing gear. Marine Pollution Bulletin 115(1-2): 91-104

Pattillo Garnham, A.; Guzmán Rojas, R.; Shek, K.L.; Dietz, H.P. 2017: Predicting levator avulsion from ICS POP-Q findings. International Urogynecology Journal 28(6): 907-911

Shen, W.; Cao, Y.; Cha, L.; Zhang, X.; Ying, X.; Zhang, W.; Ge, K.; Li, W.; Zhong, L. 2015: Predicting linear B-cell epitopes using amino acid anchoring pair composition. Biodata Mining 8: 14

Ochiai, T.; Nacher, J.C. 2016: Predicting link directionality in gene regulation from gene expression profiles using volatility-constrained correlation. Bio Systems 145: 9-18

Zheng, D.-X.; Meng, S.-C.; Liu, Q.-J.; Li, C.-T.; Shang, X.-D.; Zhu, Y.-S.; Bai, T.-J.; Xu, S.-M. 2016: Predicting liver metastasis of gastrointestinal tract cancer by diffusion-weighted imaging of apparent diffusion coefficient values. World Journal of Gastroenterology 22(10): 3031-3037

Potts, J.R.; Bastille-Rousseau, G.; Murray, D.L.; Schaefer, J.A.; Lewis, M.A. 2014: Predicting local and non-local effects of resources on animal space use using a mechanistic step selection model. Methods in Ecology and Evolution 5(3): 253-262

Wood, J.G.; Heywood, A.E.; Menzies, R.I.; McIntyre, P.B.; MacIntyre, C.R. 2015: Predicting localised measles outbreak potential in Australia. Vaccine 33(9): 1176-1181

Bradshaw, T.; Fu, R.; Bowen, S.; Zhu, J.; Forrest, L.; Jeraj, R. 2015: Predicting location of recurrence using FDG, FLT, and Cu-ATSM PET in canine sinonasal tumors treated with radiotherapy. Physics in medicine and biology 60(13): 5211-5224

Abdizadeh, H.; Atilgan, C. 2016: Predicting long term cooperativity and specific modulators of receptor interactions in human transferrin from dynamics within a single microstate. Physical Chemistry Chemical Physics: Pccp 18(11): 7916-7926

Sharma, P.K.; Chhatriwalla, A.K.; Cohen, D.J.; Jang, J.-S.; Baweja, P.; Gosch, K.; Jones, P.; Bach, R.G.; Arnold, S.V.; Spertus, J.A. 2017: Predicting long-term bleeding after percutaneous coronary intervention. Catheterization and Cardiovascular Interventions: Official Journal of the Society for Cardiac Angiography and Interventions 89(2): 199-206

Monden, R.; Stegeman, A.; Conradi, H.J.; de Jonge, P.; Wardenaar, K.J. 2016: Predicting long-term depression outcome using a three-mode principal component model for depression heterogeneity. Journal of Affective Disorders 189: 1-9

Honeybul, S.; Ho, K.M. 2016: Predicting long-term neurological outcomes after severe traumatic brain injury requiring decompressive craniectomy: a comparison of the CRASH and IMPACT prognostic models. Injury 47(9): 1886-1892

Wu, X.; Li, C.; Oberst, K.; Given, C. 2016: Predicting long-term nursing home transfer from MI choice waiver program. Geriatric Nursing 37(6): 446-452

Lu, H.-Y.; Li, T.-C.; Tu, Y.-K.; Tsai, J.-C.; Lai, H.-S.; Kuo, L.-T. 2015: Predicting long-term outcome after traumatic brain injury using repeated measurements of Glasgow Coma Scale and data mining methods. Journal of Medical Systems 39(2): 14

Rassovsky, Y.; Levi, Y.; Agranov, E.; Sela-Kaufman, M.; Sverdlik, A.; Vakil, E. 2015: Predicting long-term outcome following traumatic brain injury (TBI). Journal of Clinical and Experimental Neuropsychology 37(4): 354-366

Divchev, D.; Najjar, T.; Tillwich, F.; Rehders, T.; Palisch, H.; Nienaber, C.A. 2015: Predicting long-term outcomes of acute aortic dissection: a focus on gender. Expert Review of Cardiovascular Therapy 13(3): 325-331

Senthong, V.; Wu, Y.; Hazen, S.L.; Tang, W.H.W. 2017: Predicting long-term prognosis in stable peripheral artery disease with baseline functional capacity estimated by the Duke Activity Status Index. American Heart Journal 184: 17-25

Kliem, S.ör.; Weusthoff, S.; Hahlweg, K.; Baucom, K.J.W.; Baucom, B.R. 2015: Predicting long-term risk for relationship dissolution using nonparametric conditional survival trees. Journal of Family Psychology: Jfp: Journal of the Division of Family Psychology of the American Psychological Association 29(6): 807-817

Carluccio, G.; Bruno, M.; Collins, C.M. 2016: Predicting long-term temperature increase for time-dependent SAR levels with a single short-term temperature response. Magnetic Resonance in Medicine 75(5): 2195-2203

Shankar, S.; Gowthaman, K.; Uddin, M.S. 2016: Predicting long-term wear performance of hard-on-hard bearing couples: effect of cup orientation. Medical and Biological Engineering and Computing 54(10): 1541-1552

Santos, I.ês.; Mata, J.; Silva, M.N.; Sardinha, L.ís.B.; Teixeira, P.J. 2015: Predicting long-term weight loss maintenance in previously overweight women: a signal detection approach. Obesity 23(5): 957-964

Veron, S.; Davies, T.J.; Cadotte, M.W.; Clergeau, P.; Pavoine, S. 2017: Predicting loss of evolutionary history: Where are we?. Biological Reviews of the Cambridge Philosophical Society 92(1): 271-291

Backes, M.; Dorr, M.C.; Luitse, J.S.K.; Goslings, J.C.; Schepers, T. 2016: Predicting loss of height in surgically treated displaced intra-articular fractures of the calcaneus. International Orthopaedics 40(3): 513-518

Calverley, P.M.A. 2015: Predicting loss of lung function in healthy people. LANCET. Respiratory Medicine 3(8): 590-591

Suemitsu, K.; Iida, O.; Shiraki, T.; Suemitsu, S.; Murakami, M.; Miyamoto, M.; Izumi, M.; Nakanishi, T. 2016: Predicting loss of patency after forearm loop arteriovenous graft. Journal of Vascular Surgery 64(2): 395-401

Millar, A.C.; Lau, A.N.C.; Tomlinson, G.; Kraguljac, A.; Simel, D.L.; Detsky, A.S.; Lipscombe, L.L. 2016: Predicting low testosterone in aging men: a systematic review. Cmaj: Canadian Medical Association Journal 188(13): E321-E330

Burki, T.K. 2016: Predicting lung cancer prognosis using machine learning. LANCET. Oncology 17(10): E421

Campbell, J.; Franzen, A.; Van Landingham, C.; Lumpkin, M.; Crowell, S.; Meredith, C.; Loccisano, A.; Gentry, R.; Clewell, H. 2016: Predicting lung dosimetry of inhaled particleborne benzo[a]pyrene using physiologically based pharmacokinetic modeling. Inhalation Toxicology 28(11): 520-535

Abdollah, F.; Klett, D.E.; Sammon, J.D.; Dalela, D.; Sood, A.; Hsu, L.; Diaz, M.; Gupta, N.; Peabody, J.O.; Trinh, Q.-D.; Menon, M. 2016: Predicting lymph node invasion in patients treated with robot-assisted radical prostatectomy. Canadian Journal of Urology 23(1): 8141-8150

Agar, N.J.M.; Kirton, C.; Patel, R.S.; Martin, R.C.W.; Angelo, N.; Emanuel, P.O. 2015: Predicting lymph node metastases in cutaneous squamous cell carcinoma: use of a morphological scoring system. New Zealand Medical Journal 128(1411): 59-67

Lee, J.H.; Shin, H.J.; Yoon, J.H.; Kim, E.-K.; Moon, H.J.; Lee, H.S.; Kwon, H.J.; Kwak, J.Y. 2017: Predicting lymph node metastasis in patients with papillary thyroid carcinoma by vascular index on power Doppler ultrasound. Head and Neck 39(2): 334-340

Ilker, S.; Nilufer, C.; Firat, C.Z.; Bulent, O.; Hatice, B.; Tayfun, G. 2015: Predicting lympho-vascular space invasion in endometrial cancers with mucinous carcinomatous components. Asian Pacific Journal of Cancer Prevention: Apjcp 16(10): 4247-4250

Ju, Z.; Cao, J.-Z.; Gu, H. 2016: Predicting lysine phosphoglycerylation with fuzzy SVM by incorporating k-spaced amino acid pairs into Chou׳s general PseAAC. Journal of Theoretical Biology 397: 145-150

Ferreira, P.; Fonseca, N.A.; Dutra, I.ês.; Woods, R.; Burnside, E. 2015: Predicting malignancy from mammography findings and image-guided core biopsies. International Journal of Data Mining and Bioinformatics 11(3): 257-276

Witczak, J.; Taylor, P.; Chai, J.; Amphlett, B.; Soukias, J.-M.; Das, G.; Tennant, B.P.; Geen, J.; Okosieme, O.E. 2016: Predicting malignancy in thyroid nodules: feasibility of a predictive model integrating clinical, biochemical, and ultrasound characteristics. Thyroid Research 9: 4

Valdés, L.; San-José, E.; Ferreiro, L.ía.; Golpe, A.; González-Barcala, F.-J.; Toubes, M.ía.E.; Rodríguez-Álvarez, M.ía.X.; Álvarez-Dobaño, J.é M.; Rodríguez-Núñez, N.; Rábade, C.; Gude, F. 2015: Predicting malignant and tuberculous pleural effusions through demographics and pleural fluid analysis of patients. Clinical Respiratory Journal 9(2): 203-213

Chammas, M.C.; Macedo, T.úl.A.A.; Lo, V.W.; Gomes, A.C.; Juliano, A.; Cerri, G.G. 2016: Predicting malignant neck lymphadenopathy using color duplex sonography based on multivariate analysis. Journal of Clinical Ultrasound: Jcu 44(9): 587-594

Zhang, H.; Li, H.; Ma, Q.; Yang, F-Yan.; Diao, T-Yu. 2016: Predicting malignant transformation of esophageal squamous cell lesions by combined biomarkers in an endoscopic screening program. World Journal of Gastroenterology 22(39): 8770-8778

La Delfa, N.J.; Potvin, J.R. 2016: Predicting manual arm strength: a direct comparison between artificial neural network and multiple regression approaches. Journal of Biomechanics 49(4): 602-605

Bellenger, C.R.; Fuller, J.T.; Nelson, M.J.; Hartland, M.; Buckley, J.D.; Debenedictis, T.A. 2015: Predicting maximal aerobic speed through set distance time-trials. European Journal of Applied Physiology 115(12): 2593-2598

Grilo, C.M.; White, M.A.; Masheb, R.M.; Gueorguieva, R. 2015: Predicting meaningful outcomes to medication and self-help treatments for binge-eating disorder in primary care: The significance of early rapid response. Journal of Consulting and Clinical Psychology 83(2): 387-394

Fowler, S.M.; Schmidt, H.; van de Ven, R.; Wynn, P.; Hopkins, D.L. 2015: Predicting meat quality traits of ovine m. semimembranosus, both fresh and following freezing and thawing, using a hand held Raman spectroscopic device. Meat Science 108: 138-144

Paolino, N.D.; Artino, A.R.; Saguil, A.; Dong, T.; Durning, S.J.; DeZee, K.J. 2015: Predicting medical school and internship success: does the quality of the research and clinical experience matter?. Military medicine 180(4 Suppl): 12-17

Turner, A.P.; Roubinov, D.S.; Atkins, D.C.; Haselkorn, J.K. 2016: Predicting medication adherence in multiple sclerosis using telephone-based home monitoring. Disability and Health Journal 9(1): 83-89

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Trenerry, M.R.; Meador, K.J. 2015: Predicting memory change after temporal lobectomy for epilepsy. Neurology 84(15): 1508-1509

MacDonald, O.K.; D'Amico, A.V.; Sadetsky, N.; Shrieve, D.C.; Bakst, A.W.; Carroll, P.R. 2016: Predicting men at high risk for PSA failure after salvage radiotherapy for rising PSA following prostatectomy. Journal of Clinical Oncology 24(18_Suppl): 4570-4570

Nederhof, E.; van Oort, F.V.A.; Bouma, E.M.C.; Laceulle, O.M.; Oldehinkel, A.J.; Ormel, J. 2015: Predicting mental disorders from hypothalamic-pituitary-adrenal axis functioning: a 3-year follow-up in the TRAILS study. Psychological Medicine 45(11): 2403-2412

Timmerman, L.; Timman, R.; Laging, M.; Zuidema, W.C.; Beck, D.K.; IJzermans, J.N.M.; Busschbach, J.J.V.; Weimar, W.; Massey, E.K. 2016: Predicting mental health after living kidney donation: the importance of psychological factors. British Journal of Health Psychology 21(3): 533-554

De Müllenheim, P.-Y.; Dumond, R.ém.; Gernigon, M.; Mahé, G.; Lavenu, A.; Bickert, S.; Prioux, J.; Noury-Desvaux, B.én.éd.; Le Faucheur, A. 2016: Predicting metabolic rate during level and uphill outdoor walking using a low-cost GPS receiver. Journal of Applied Physiology 121(2): 577-588

Karimi-Alavijeh, F.; Jalili, S.; Sadeghi, M. 2016: Predicting metabolic syndrome using decision tree and support vector machine methods. Arya Atherosclerosis 12(3): 146-152

Couturier, J.; Trolet, J.; Hupe, P.; Desjardins, L.; Mariani, P.; Sastre-Garau, X.; Asselain, B.; Barillot, E.; Saule, S.; Piperno-Neumann, S. 2016: Predicting metastasis in uveal melanoma: Identification of a prognostic classifier by genomic profiling using array-CGH of 78 ocular tumors (OT) and 66 liver metastases (LM). Journal of Clinical Oncology 26(15_Suppl): 9041-9041

Zhang, W.; Le, T.Duy.; Liu, L.; Zhou, Z-Hua.; Li, J. 2016: Predicting miRNA Targets by Integrating Gene Regulatory Knowledge with Expression Profiles. Plos one 11(4): E0152860

Lan, W.; Wang, J.; Li, M.; Liu, J.; Wu, F-Xiang.; Pan, Y. 2018: Predicting MicroRNA-Disease Associations Based on Improved MicroRNA and Disease Similarities. Ieee/Acm Transactions on Computational Biology and Bioinformatics 15(6): 1774-1782

Li, C.; Lim, K.M.K.; Chng, K.R.; Nagarajan, N. 2016: Predicting microbial interactions through computational approaches. Methods 102: 12-19

Oliver, D.M.; Porter, K.D.H.; Pachepsky, Y.A.; Muirhead, R.W.; Reaney, S.M.; Coffey, R.; Kay, D.; Milledge, D.G.; Hong, E.; Anthony, S.G.; Page, T.; Bloodworth, J.W.; Mellander, P-Erik.; Carbonneau, P.E.; McGrane, S.J.; Quilliam, R.S. 2016: Predicting microbial water quality with models: Over-arching questions for managing risk in agricultural catchments. Science of the Total Environment 544: 39-47

Pardi, D.S. 2015: Predicting microscopic colitis before colon biopsies: a look into the crystal ball?. Clinical Gastroenterology and Hepatology: the Official Clinical Practice Journal of the American Gastroenterological Association 13(6): 1132-1133

Horobin, R.W. 2015: Predicting mitochondrial targeting by small molecule xenobiotics within living cells using QSAR models. Methods in Molecular Biology 1265: 13-23

Spaan, M.H.; Vrieling, A.H.; van de Berg, P.; Dijkstra, P.U.; van Keeken, H.G. 2017: Predicting mobility outcome in lower limb amputees with motor ability tests used in early rehabilitation. Prosthetics and Orthotics International 41(2): 171-177

Conti, S.; Cecchini, M. 2016: Predicting molecular self-assembly at surfaces: a statistical thermodynamics and modeling approach. Physical Chemistry Chemical Physics: Pccp 18(46): 31480-31493

Rosenberg, M.D.; Finn, E.S.; Constable, R.T.; Chun, M.M. 2015: Predicting moment-to-moment attentional state. Neuroimage 114: 249-256

Yadav, J.; Yadav, S.Kumar.; Kumar, S.; Baxla, R.George.; Sinha, D.Kumar.; Bodra, P.; Besra, R.Chandra.; Baski, B.Mani.; Prakash, O.; Anand, A. 2016: Predicting morbidity and mortality in acute pancreatitis in an Indian population: a comparative study of the BISAP score, Ranson's score and CT severity index. Gastroenterology Report 4(3): 216-220

Rothnie, K.J.; Smeeth, L.; Pearce, N.; Herrett, E.; Timmis, A.; Hemingway, H.; Wedzicha, J.; Quint, J.K. 2016: Predicting mortality after acute coronary syndromes in people with chronic obstructive pulmonary disease. Heart 102(18): 1442-1448

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Nieuweboer, A.J.M.; Smid, M.; de Graan, A.-J.M.; Elbouazzaoui, S.; de Bruijn, P.; Martens, J.W.; Mathijssen, R.H.J.; van Schaik, R.H.N. 2015: Predicting paclitaxel-induced neutropenia using the DMET platform. Pharmacogenomics 16(11): 1231-1241

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Krishnaswamy, A.; Tuzcu, E.M. 2015: Predicting paravalvular leak after balloon-expandable TAVR. Catheterization and Cardiovascular Interventions: Official Journal of the Society for Cardiac Angiography and Interventions 86(1): 152-153

Hiemstra-van Mastrigt, S.; Groenesteijn, L.; Vink, P.; Kuijt-Evers, L.F.M. 2017: Predicting passenger seat comfort and discomfort on the basis of human, context and seat characteristics: a literature review. Ergonomics 60(7): 889-911

Crauste, F.; Terry, E.; Mercier, I.L.; Mafille, J.; Djebali, S.; Andrieu, T.; Mercier, B.; Kaneko, G.; Arpin, C.; Marvel, J.; Gandrillon, O. 2015: Predicting pathogen-specific CD8 T cell immune responses from a modeling approach. Journal of Theoretical Biology 374: 66-82

Groheux, D. 2014: Predicting pathological complete response in breast cancer early. LANCET. Oncology 15(13): 1415-1416

Ryan, J.E.; Warrier, S.K.; Lynch, A.C.; Ramsay, R.G.; Phillips, W.A.; Heriot, A.G. 2016: Predicting pathological complete response to neoadjuvant chemoradiotherapy in locally advanced rectal cancer: a systematic review. Colorectal Disease: the Official Journal of the Association of Coloproctology of Great Britain and Ireland 18(3): 234-246

Vederhus, J.-K.år.; Zemore, S.E.; Rise, J.; Clausen, T.; Høie, M. 2015: Predicting patient post-detoxification engagement in 12-step groups with an extended version of the theory of planned behavior. Addiction Science and Clinical Practice 10: 15

Li, B.; Cairns, J.A.; Robb, M.L.; Johnson, R.J.; Watson, C.J.E.; Forsythe, J.L.; Oniscu, G.C.; Ravanan, R.; Dudley, C.; Roderick, P.; Metcalfe, W.; Tomson, C.R.; Bradley, J.A. 2016: Predicting patient survival after deceased donor kidney transplantation using flexible parametric modelling. Bmc Nephrology 17(1): 51

Quaglino, P.; Matthiessen, L.W.; Curatolo, P.; Muir, T.; Bertino, G.; Kunte, C.; Odili, J.; Rotunno, R.; Humphreys, A.C.; Letulé, V.; Marenco, F.; Cuthbert, C.; Albret, R.; Benazzo, M.; De Terlizzi, F.; Gehl, J. 2015: Predicting patients at risk for pain associated with electrochemotherapy. Acta Oncologica 54(3): 298-306

Schoell, S.L.; Doud, A.N.; Weaver, A.A.; Barnard, R.T.; Meredith, J.W.; Stitzel, J.D.; Martin, R.S. 2015: Predicting patients that require care at a trauma center: analysis of injuries and other factors. Injury 46(4): 558-563

Koshy, M.; Battafarano, R.; Burrows, W.; Krasna, M.; Greenwald, B.; Mannuel, H.; Suntharalingam, M. 2016: Predicting patterns of failure in esophageal cancer following tri-modality therapy: Why histology matters. Journal of Clinical Oncology 26(15_Suppl): 4553-4553

Bertram, J.; Gomez, K.; Masel, J. 2017: Predicting patterns of long-term adaptation and extinction with population genetics. Evolution; International Journal of Organic Evolution 71(2): 204-214

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Indraccolo, U.; Scutiero, G.; Greco, P. 2016: Predicting pelvic visceral hypersensitivity from the discomfort of Lugol' test during colposcopy. European Review for Medical and Pharmacological Sciences 20(13): 2762-2763

Bello, M.; Campos-Rodriguez, R.; Rojas-Hernandez, S.; Contis-Montes de Oca, A.; Correa-Basurto, J.é 2015: Predicting peptide vaccine candidates against H1N1 influenza virus through theoretical approaches. Immunologic Research 62(1): 3-15

Chen, T.S.; Petrey, D.; Garzon, J.I.; Honig, B. 2015: Predicting peptide-mediated interactions on a genome-wide scale. Plos Computational Biology 11(5): E1004248

George, N.R.; Steffen, A.M. 2017: Predicting perceived medication-related hassles in dementia family caregivers. Dementia 16(6): 797-810

Oexle, N.; Ajdacic-Gross, V.; Müller, M.; Rodgers, S.; Rössler, W.; Rüsch, N. 2015: Predicting perceived need for mental health care in a community sample: an application of the self-regulatory model. Social Psychiatry and Psychiatric Epidemiology 50(10): 1593-1600

Cameron, E.; French, D.P. 2016: Predicting perceived safety to drive the morning after drinking: the importance of hangover symptoms. Drug and Alcohol Review 35(4): 442-446

Wang, F.; Huang, J.; Lv, Y.; Ma, X.; Yang, B.; Wang, E.; Du, B.; Li, W.; Song, Y. 2016: Predicting perceptual learning from higher-order cortical processing. Neuroimage 124(Part A): 682-692

Radicchi, F. 2015: Predicting percolation thresholds in networks. Physical Review. e Statistical Nonlinear and Soft Matter Physics 91(1): 010801

Dippong, J.; Kalkhoff, W. 2015: Predicting performance expectations from affective impressions: linking affect control theory and status characteristics theory. Social Science Research 50: 1-14

Stegers-Jager, K.M.; Themmen, A.P.N.; Cohen-Schotanus, J.; Steyerberg, E.W. 2015: Predicting performance: relative importance of students' background and past performance. Medical Education 49(9): 933-945

Rahbari, A.; Montazerian, H.; Davoodi, E.; Homayoonfar, S. 2017: Predicting permeability of regular tissue engineering scaffolds: scaling analysis of pore architecture, scaffold length, and fluid flow rate effects. Computer Methods in Biomechanics and Biomedical Engineering 20(3): 231-241

Manganaro, A.; Pizzo, F.; Lombardo, A.; Pogliaghi, A.; Benfenati, E. 2016: Predicting persistence in the sediment compartment with a new automatic software based on the k-Nearest Neighbor (k-NN) algorithm. Chemosphere 144: 1624-1630

Rohde, P.; Stice, E.; Gau, J.M. 2017: Predicting persistence of eating disorder compensatory weight control behaviors. International Journal of Eating Disorders 50(5): 561-568

Bushnell, G.A.; Stürmer, T.; White, A.; Pate, V.; Swanson, S.A.; Azrael, D.; Miller, M. 2016: Predicting persistence to antidepressant treatment in administrative claims data: Considering the influence of refill delays and prior persistence on other medications. Journal of Affective Disorders 196: 138-147

Fan, H.; Zhu, Q.; Ma, G.; Shen, C.; Zhang, B.; Wang, W. 2016: Predicting personality disorder functioning styles by the Chinese Adjective Descriptors of Personality: a preliminary trial in healthy people and personality disorder patients. Bmc Psychiatry 16: 302

Chen, H.; Grieneisen, M.L.; Zhang, M. 2016: Predicting pesticide removal efficacy of vegetated filter strips: a meta-regression analysis. Science of the Total Environment 548-549: 122-130

Haraya, K.; Tachibana, T.; Nezu, J. 2017: Predicting pharmacokinetic profile of therapeutic antibodies after iv injection from only the data after sc injection in cynomolgus monkey. Xenobiotica; the Fate of Foreign Compounds in Biological Systems 47(3): 194-201

Yu, M.-M.; Gao, Z.-W.; Chen, X.-Y.; Zhong, D.-F. 2014: Predicting pharmacokinetics of anti-cancer drug, famitinib in human using physiologically based pharmacokinetic model. Yao Xue Xue Bao 49(12): 1684-1688

Nightingale, T.E.; Walhin, J.P.; Thompson, D.; Bilzon, J.L.J. 2015: Predicting physical activity energy expenditure in wheelchair users with a multisensor device. Bmj Open Sport and Exercise Medicine 1(1)

Drain, J.; Billing, D.; Neesham-Smith, D.; Aisbett, B. 2016: Predicting physiological capacity of human load carriage - a review. Applied Ergonomics 52: 85-94

Jain, S.; Bader, G.D. 2016: Predicting physiologically relevant SH3 domain mediated protein-protein interactions in yeast. Bioinformatics 32(12): 1865-1872

Nakonezny, P.A.; Mayes, T.L.; Byerly, M.J.; Emslie, G.J. 2015: Predicting placebo response in adolescents with major depressive disorder: the Adolescent Placebo Impact Composite Score (APICS). Journal of Psychiatric Research 68: 346-353

Flora, P.K.; Brawley, L.R.; Sessford, J.D.; Cary, M.A.; Gyurcsik, N.C. 2016: Predicting planned physical activity of individuals with arthritis: a self-regulatory perspective. Journal of Health Psychology 21(11): 2684-2694

Bahlai, C.A.; Landis, D.A. 2016: Predicting plant attractiveness to pollinators with passive crowdsourcing. Royal Society Open Science 3(6): 150677

Brown, K.A.; Parks, K.E.; Bethell, C.A.; Johnson, S.E.; Mulligan, M. 2015: Predicting plant diversity patterns in Madagascar: understanding the effects of climate and land cover change in a biodiversity hotspot. Plos one 10(4): E0122721

Kader, M.; Lamb, D.T.; Mahbub, K.R.; Megharaj, M.; Naidu, R. 2016: Predicting plant uptake and toxicity of lead (Pb) in long-term contaminated soils from derived transfer functions. Environmental Science and Pollution Research International 23(15): 15460-15470

Lamb, D.T.; Kader, M.; Ming, H.; Wang, L.; Abbasi, S.; Megharaj, M.; Naidu, R. 2016: Predicting plant uptake of cadmium: validated with long-term contaminated soils. Ecotoxicology 25(8): 1563-1574

Skelton, R.P.; West, A.G.; Dawson, T.E. 2015: Predicting plant vulnerability to drought in biodiverse regions using functional traits. Proceedings of the National Academy of Sciences of the United States of America 112(18): 5744-5749

James, L.S.; Sakata, J.T. 2015: Predicting plasticity: acute context-dependent changes to vocal performance predict long-term age-dependent changes. Journal of Neurophysiology 114(4): 2328-2339

Coyle, W.L.; Guillemain, P.; Kergomard, J.; Dalmont, J.-P. 2015: Predicting playing frequencies for clarinets: a comparison between numerical simulations and simplified analytical formulas. Journal of the Acoustical Society of America 138(5): 2770-2781

Tanaka, T.; Shinya, T.; Sato, S.; Mitsuhashi, T.; Ichimura, K.; Soh, J.; Toyooka, S.; Kaji, M.; Miyoshi, S.; Kanazawa, S. 2015: Predicting pleural invasion using HRCT and 18F-FDG PET/CT in lung adenocarcinoma with pleural contact. Annals of Nuclear Medicine 29(9): 757-765

Abdullah, R.; Alhusainy, W.; Woutersen, J.; Rietjens, I.M.C.M.; Punt, A. 2016: Predicting points of departure for risk assessment based on in vitro cytotoxicity data and physiologically based kinetic (PBK) modeling: the case of kidney toxicity induced by aristolochic acid i. Food and Chemical Toxicology: An International Journal Published for the British Industrial Biological Research Association 92: 104-116

Wasserman, J.C.; Wasserman, M.A.él.V.; Barrocas, P.R.G.; Almeida, A.M. 2016: Predicting pollutant concentrations in the water column during dredging operations: Implications for sediment quality criteria. Marine Pollution Bulletin 108(1-2): 24-32

Musto, P.; Simeon, V.; Grossi, A.; Gay, F.; Bringhen, S.; Larocca, A.; Guariglia, R.; Pietrantuono, G.; Villani, O.; D'Arena, G.; Cuomo, C.; Musto, C.; Morabito, F.; Petrucci, M.T.; Offidani, M.; Zamagni, E.; Tacchetti, P.; Conticello, C.; Milone, G.; Palumbo, A.; Cavo, M.; Boccadoro, M. 2015: Predicting poor peripheral blood stem cell collection in patients with multiple myeloma receiving pre-transplant induction therapy with novel agents and mobilized with cyclophosphamide plus granulocyte-colony stimulating factor: results from a Gruppo Italiano Malattie EMatologiche dell'Adulto Multiple Myeloma Working Party study. Stem Cell Research and Therapy 6: 64

Brockmann, P.E.; Schlaud, M.; Poets, C.F.; Urschitz, M.S. 2015: Predicting poor school performance in children suspected for sleep-disordered breathing. Sleep Medicine 16(9): 1077-1083

Wilson, K.B.; Overholt, M.F.; Hogan, E.K.; Schwab, C.; Shull, C.M.; Ellis, M.; Grohmann, N.S.; Dilger, A.C.; Boler, D.D. 2016: Predicting pork loin chop yield using carcass and loin characteristics. Journal of Animal Science 94(11): 4903-4910

Balage, J.M.; da Luz E Silva, S.; Gomide, C.A.; Bonin, M.d.N.; Figueira, A.C. 2015: Predicting pork quality using Vis/NIR spectroscopy. Meat Science 108: 37-43

Saragih Turnip, S.; Sörbom, D.; Hauff, E. 2016: Predicting positive mental health in internally displaced persons in Indonesia: the roles of economic improvement and exposure to violent conflict. Psychology Health and Medicine 21(3): 286-294

Yong, W.S.; Chang, M.H.; Chen, W.J.J. 2016: Predicting possibility of positive non-sentinel nodes in sentinel node-positive breast cancer patients: A Singapore series. Journal of Clinical Oncology 29(27_Suppl): 51-51

Papachristos, A.; Howard, T.; Thomson, B.N.; Thomas, P.R. 2018: Predicting post-endoscopic retrograde cholangiopancreatography pancreatitis using the 4-h serum lipase level. ANZ Journal of Surgery 88(1-2): 82-86

Haagen, J.F.G.; Ter Heide, F.J.J.; Mooren, T.M.; Knipscheer, J.W.; Kleber, R.J. 2017: Predicting post-traumatic stress disorder treatment response in refugees: Multilevel analysis. British Journal of Clinical Psychology 56(1): 69-83

Huddar, V.; Rajan, V.; Bhattacharya, S.; Roy, S. 2014: Predicting postoperative acute respiratory failure in critical care using nursing notes and physiological signals. Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference 2014: 2702-2705

Kashani, R.G.; Sareh, S.; Genovese, B.; Hershey, C.; Rezentes, C.; Shemin, R.; Buch, E.; Benharash, P. 2015: Predicting postoperative atrial fibrillation using CHA2DS2-VASc scores. Journal of Surgical Research 198(2): 267-272

Chan, M.T.V.; Gin, T. 2015: Predicting postoperative cardiac complications using automated endothelial function test. Hong Kong Medical Journal 21(Suppl 6): 17-18

Chen, Y.; Cao, W.; Gao, X.; Ong, H.; Ji, T. 2015: Predicting postoperative complications of head and neck squamous cell carcinoma in elderly patients using random forest algorithm model. Bmc Medical Informatics and Decision Making 15: 44

Gopman, J.M.; Djajadiningrat, R.S.; Baumgarten, A.S.; Espiritu, P.N.; Horenblas, S.; Zhu, Y.; Protzel, C.; Pow-Sang, J.M.; Kim, T.; Sexton, W.J.; Poch, M.A.; Spiess, P.E. 2015: Predicting postoperative complications of inguinal lymph node dissection for penile cancer in an international multicentre cohort. Bju International 116(2): 196-201

Swenson, C.W.; Lanham, M.S.; Morgan, D.M.; Berger, M.B. 2015: Predicting postoperative day 1 hematocrit levels after uncomplicated hysterectomy. International Journal of Gynaecology and Obstetrics: the Official Organ of the International Federation of Gynaecology and Obstetrics 130(1): 19-22

Visser, L.; Prent, A.; van der Laan, M.J.; van Leeuwen, B.L.; Izaks, G.J.; Zeebregts, C.J.; Pol, R.A. 2015: Predicting postoperative delirium after vascular surgical procedures. Journal of Vascular Surgery 62(1): 183-189

Nagamatsu, Y.; Sueyoshi, S.; Tsubuku, T.; Kawasaki, M.; Akagi, Y. 2015: Predicting postoperative exercise capacity after major lung resection. Surgery Today 45(12): 1501-1508

Nomura, K.; Yamanaka, Y.; Sekine, Y.; Yamamoto, H.; Esu, Y.; Hara, M.; Hasegawa, M.; Shinnabe, A.; Kanazawa, H.; Kakuta, R.; Ozawa, D.; Hidaka, H.; Katori, Y.; Yoshida, N. 2017: Predicting postoperative fever and bacterial colonization on packing material following endoscopic endonasal surgery. European Archives of Oto-Rhino-Laryngology: Official Journal of the European Federation of Oto-Rhino-Laryngological Societies: Affiliated with the German Society for Oto-Rhino-Laryngology - Head and Neck Surgery 274(1): 167-173

Galarraga C, O.A.; Vigneron, V.; Dorizzi, B.; Khouri, N.éj.; Desailly, E. 2017: Predicting postoperative gait in cerebral palsy. Gait and Posture 52: 45-51

Galarraga C, O.A.; Vigneron, V.; Dorizzi, B.; Khouri, N.; Desailly, E. 2015: Predicting postoperative knee flexion during gait of cerebral palsy children. Computer Methods in Biomechanics and Biomedical Engineering 18 Suppl. 1: 1940-1941

Silins, V.; Brasher, C.; Antus, F.; Michelet, D.é; Hilly, J.; Grace, R.; Dahmani, S. 2017: Predicting postoperative morphine consumption in children. Anaesthesia Critical Care and Pain Medicine 36(3): 179-184

Kang, B.H.; Hwang, S.Y.; Kim, J.Y.; Hong, Y.A.; Jung, M.Y.; Lee, E.A.; Lee, J.E.; Lee, J.B.; Ko, G.J.; Pyo, H.J.; Kwon, Y.J. 2015: Predicting postoperative total calcium requirements after parathyroidectomy in secondary hyperparathyroidism. Korean Journal of Internal Medicine 30(6): 856-864

Macky, T.A.; Mohamed, A.M.H.; Emarah, A.M.; Osman, A.A.; Gado, A.S. 2015: Predicting postoperative visual outcomes in cataract patients with maculopathy. Indian Journal of Ophthalmology 63(10): 775-778

Van Meijel, E.P.M.; Gigengack, M.R.; Verlinden, E.; Opmeer, B.C.; Heij, H.A.; Goslings, J.C.; Bloemers, F.W.; Luitse, J.S.K.; Boer, F.; Grootenhuis, M.A.; Lindauer, R.ón.J.L. 2015: Predicting posttraumatic stress disorder in children and parents following accidental child injury: evaluation of the Screening Tool for Early Predictors of Posttraumatic Stress Disorder (STEPP). Bmc Psychiatry 15: 113

Pokharel, K.P.; Ludwig, T.; Storch, I. 2016: Predicting potential distribution of poorly known species with small database: the case of four-horned antelope Tetracerus quadricornis on the Indian subcontinent. Ecology and Evolution 6(8): 2297-2307

Alimi, T.O.; Fuller, D.O.; Qualls, W.A.; Herrera, S.V.; Arevalo-Herrera, M.; Quinones, M.L.; Lacerda, M.V.G.; Beier, J.C. 2015: Predicting potential ranges of primary malaria vectors and malaria in northern South America based on projected changes in climate, land cover and human population. Parasites and Vectors 8: 431

Patterson, W.L.; Peoples, B.D.; Gesten, F.C. 2015: Predicting potentially preventable hospital readmissions following bariatric surgery. Surgery for Obesity and Related Diseases: Official Journal of the American Society for Bariatric Surgery 11(4): 866-872

Blumenstock, J.; Cadamuro, G.; On, R. 2015: Predicting poverty and wealth from mobile phone metadata. Science 350(6264): 1073-1076

Hwang, J.Won.; Bae, Y.Seok.; Kang, M.Seon.; Kim, J.Hyun.; Jee, S.Ryong.; Lee, S.Heon.; An, M.Sung.; Kim, K.Hee.; Bae, K.Beom.; Kim, B.; Seol, S.Young. 2016: Predicting pre- and post-resectional histologic discrepancies in gastric low-grade dysplasia: A comparison of white-light and magnifying endoscopy. Journal of Gastroenterology and Hepatology 31(2): 394-402

Gomes, M.S.; Böttcher, D.E.; Scarparo, R.K.; Morgental, R.D.; Waltrick, S.B.G.; Ghisi, A.C.; Rahde, N.M.; Borba, M.G.; Blomberg, L.C.; Figueiredo, J.A.P. 2017: Predicting pre- and postoperative pain of endodontic origin in a southern Brazilian subpopulation: an electronic database study. International Endodontic Journal 50(8): 729-739

Chappell, L.C.; Sandall, J.; Barnard, A.M.; McManus, R.J. 2015: Predicting pre-eclampsia. Bmj 351: H6349

Jauniaux, E.; Steer, P. 2016: Predicting pre-eclampsia: 100 years of trying and failing. BJOG: an international journal of obstetrics and gynaecology 123(7): 1066

Steer, P.J. 2016: Predicting pre-eclampsia: dealing with both complex models and complex variables. Bjog: An International Journal of Obstetrics and Gynaecology 123(7): 1065

Wang, Y.; Bian, J.M.; Wang, S.N.; Nie, S.Y. 2016: Predicting precipitation on nonpoint source pollutant exports in the source area of the Liao River, China. Water Science and Technology: a Journal of the International Association on Water Pollution Research 74(4): 876-887

Levin, D.; Jun, S.H.; Dahan, M.H. 2015: Predicting pregnancy in women undergoing in-vitro fertilization with basal serum follicle stimulating hormone levels between 10.0 and 11.9 IU/L. Journal of the Turkish German Gynecological Association 16(1): 5-10

Racine, N.M.; Pillai Riddell, R.R.; Flora, D.B.; Taddio, A.; Garfield, H.; Greenberg, S. 2016: Predicting preschool pain-related anticipatory distress: the relative contribution of longitudinal and concurrent factors. Pain 157(9): 1918-1932

Meisel, M.K.; Goodie, A.S. 2015: Predicting prescription drug misuse in college students' social networks. Addictive behaviors 45: 110-112

Flanagan, M. 1993: Predicting pressure sore risk. Journal of Wound Care 2(4): 215-218

Bao, J.Lucas.; Zhang, X.; Truhlar, D.G. 2016: Predicting pressure-dependent unimolecular rate constants using variational transition state theory with multidimensional tunneling combined with system-specific quantum RRK theory: a definitive test for fluoroform dissociation. Physical Chemistry Chemical Physics: Pccp 18(25): 16659-16670

NeMoyer, A.; Brooks Holliday, S.; Goldstein, N.E.S.; McKitten, R.L. 2016: Predicting probation revocation and residential facility placement at juvenile probation review hearings: Youth-specific and hearing-specific factors. Law and human behavior 40(1): 97-105

Creswell, K.G.; Bachrach, R.L.; Wright, A.G.C.; Pinto, A.; Ansell, E. 2016: Predicting problematic alcohol use with the DSM-5 alternative model of personality pathology. Personality Disorders 7(1): 103-111

Gul, Z.; Alazem, K.; Li, I.; Monga, M. 2016: Predicting procedural pain after ureteroscopy: does hydrodistention play a role?. International Braz J Urol: Official Journal of the Brazilian Society of Urology 42(4): 734-739

Lee, C.-W.; Lin, Y.-H.; Liu, H.-M.; Wang, Y.-F.; Chen, Y.-F.; Wang, J.-L. 2016: Predicting procedure successful rate and 1-year patency after endovascular recanalization for chronic carotid artery occlusion by CT angiography. International Journal of Cardiology 221: 772-776

Avieli, H.; Ben-David, S.; Levy, I. 2016: Predicting professional quality of life among professional and volunteer caregivers. Psychological Trauma: Theory Research Practice and Policy 8(1): 80-87

Quigley, D.A.; Kristensen, V. 2015: Predicting prognosis and therapeutic response from interactions between lymphocytes and tumor cells. Molecular Oncology 9(10): 2054-2062

Hirano, Y.; Kayano, H.; Kawamata, T.; Toshida, T.; Ueda, H.; Ando, H.; Ozawa, M.; Katagiri, T.; Abe, K. 2006: Predicting prognosis based on the shape of the left ventricular cavity in dilated cardiomyopathy: analysis using rate of improvement in the circle index. Journal of Medical Ultrasonics 33(4): 217-224

Elamin, M.; Bede, P.; Montuschi, A.; Pender, N.; Chio, A.; Hardiman, O. 2015: Predicting prognosis in amyotrophic lateral sclerosis: a simple algorithm. Journal of Neurology 262(6): 1447-1454