+ Site Statistics
+ Search Articles
+ Subscribe to Site Feeds
Most Shared
PDF Full Text
+ PDF Full Text
Request PDF Full Text
+ Follow Us
Follow on Facebook
Follow on Twitter
Follow on LinkedIn
+ Translate
+ Recently Requested

Get PDF Full Texts from EurekaMag Chapter 55099

Chapter 55099 provides scholary research titles of which PDF Full Texts are available through EurekaMag.

Martí, S.; Andrés, J.; Moliner, V.; Silla, E.; Tuñón, Iñaki.; Bertran, J., 2008:
Predicting an improvement of secondary catalytic activity of promiscuous isochorismate pyruvate lyase by computational design

Roos, A-Marie.; Abdool, Z.; Thakar, R.; Sultan, A.H., 2012:
Predicting anal sphincter defects: the value of clinical examination and manometry

Wang, J.; Ke, C.; Jiang, Q.; Zhang, C.; Snapinn, S., 2012:
Predicting analysis time in event-driven clinical trials with event-reporting lag

Stinear, C.M.; Byblow, W.D., 2015:
Predicting and accelerating motor recovery after stroke

Khorana, A.A., 2012:
Predicting and accounting for VTE in phase I cancer studies

Huang, H-Lin.; Lin, I-Che.; Liou, Y-Fan.; Tsai, C-Ta.; Hsu, K-Ti.; Huang, W-Lin.; Ho, S-Jang.; Ho, S-Ying., 2011:
Predicting and analyzing DNA-binding domains using a systematic approach to identifying a set of informative physicochemical and biochemical properties

Rapanoel, H.A.; Mazandu, G.K.; Mulder, N.J., 2014:
Predicting and analyzing interactions between Mycobacterium tuberculosis and its human host

Yao, Q.; Gao, J.; Bollinger, C.; Thelen, J.J.; Xu, D., 2012:
Predicting and analyzing protein phosphorylation sites in plants using musite

Thompson, K.; Dockery, P.; Horobin, R.W., 2013:
Predicting and avoiding subcellular compartmentalization artifacts arising from acetoxymethyl ester calcium imaging probes. The case of fluo-3 AM and a general account of the phenomenon including a problem avoidance chart

Glare, P., 2011:
Predicting and communicating prognosis in palliative care

Engelhardt, E.G.; Garvelink, M.M.; de Haes, J.Hanneke.C.J.M.; van der Hoeven, J.J.M.; Smets, E.M.A.; Pieterse, A.H.; Stiggelbout, A.M., 2014:
Predicting and communicating the risk of recurrence and death in women with early-stage breast cancer: a systematic review of risk prediction models

Masuda, N.; Holme, P., 2013:
Predicting and controlling infectious disease epidemics using temporal networks

Griffin, M.J., 2014:
Predicting and controlling risks from human exposures to vibration and mechanical shock: flag waving and flag weaving

Ambroise-Thomas, P., 2009:
Predicting and controlling trends for rational nationwide distribution of healthcare services

Bhanpuri, N.H.; Okamura, A.M.; Bastian, A.J., 2014:
Predicting and correcting ataxia using a model of cerebellar function

Mangoni, A.A., 2012:
Predicting and detecting adverse drug reactions in old age: challenges and opportunities

Estes, J.A.; Brashares, J.S.; Power, M.E., 2014:
Predicting and detecting reciprocity between indirect ecological interactions and evolution

Scadding, G., 2007 :
Predicting and establishing the clinical efficacy of a histamine h(1)-receptor antagonist : desloratadine, the model paradigm

Zhao, Q.; Edrich, T.; Paschalidis, I.Ch., 2014:
Predicting and evaluating the effect of bivalirudin in cardiac surgical patients

Arden, M.A.; Armitage, C.J., 2008:
Predicting and explaining transtheoretical model stage transitions in relation to condom-carrying behaviour

Anderson, P., 2010:
Predicting and facilitating survival of pediatric cancer patients: the ALC story

Rafi, S.B.; Hearn, B.R.; Vedantham, P.; Jacobson, M.P.; Renslo, A.R., 2012:
Predicting and improving the membrane permeability of peptidic small molecules

Bohil, C.J.; Higgins, N.A.; Keebler, J.R., 2015:
Predicting and interpreting identification errors in military vehicle training using multidimensional scaling

Ilyina, R.Yu.; Pasynkova, O.O.; Ziganshina, L.E., 2014:
Predicting and managing adverse reactions of psychotropic drugs

Selby, N.M.; McIntyre, C.W., 2013:
Predicting and managing complications of renal replacement therapy in the critically ill

Govoni, M.; Caporali, R., 2012:
Predicting and managing the progression of structural damage in rheumatoid arthritis: where do we stand?

Reck, M.; Barlesi, F.; Crinò, L.; Henschke, C.I.; Isla, D.; Stiebeler, S.; Spigel, D.R., 2012:
Predicting and managing the risk of pulmonary haemorrhage in patients with NSCLC treated with bevacizumab: a consensus report from a panel of experts

Richards, A.; Hannon, E.M.; Derakshan, N., 2011:
Predicting and manipulating the incidence of inattentional blindness

Belaire, J.Amy.; Kreakie, B.J.; Keitt, T.; Minor, E., 2014:
Predicting and mapping potential Whooping Crane stopover habitat to guide site selection for wind energy projects

Hong, S.Young.; Minasny, B.; Han, K.Hwa.; Kim, Y.; Lee, K., 2013:
Predicting and mapping soil available water capacity in Korea

Biggar, J.W.; Dutt, G.R.; Riggs, R.L., 1967:
Predicting and measuring the solubility of p,p'- DDT in water

Stadler, W.; Schubotz, R.I.; von Cramon, D.Yves.; Springer, A.; Graf, M.; Prinz, W., 2011:
Predicting and memorizing observed action: differential premotor cortex involvement

Westhof, E.; Masquida, Bît.; Jossinet, F., 2011:
Predicting and modeling RNA architecture

Gross, R., 2008:
Predicting and monitoring antiretroviral adherence

Thoeny, H.C.; Ross, B.D., 2010:
Predicting and monitoring cancer treatment response with diffusion-weighted MRI

Fredin, M.Fritsch.; Hultin, L.; Hyberg, G.; Rehnström, E.; Hultgren Hörnquist, E.; Melgar, S.; Jansson, L., 2007:
Predicting and monitoring colitis development in mice by micro-computed tomography

Harper, K.C.; Sigman, M.S., 2011:
Predicting and optimizing asymmetric catalyst performance using the principles of experimental design and steric parameters

Hansalia, R.J.; Duvall, W.L.; Mehta, D., 2009:
Predicting and optimizing response to cardiac resynchronization therapy beyond QRS duration: expanding role of echocardiography

Liss, G.; Rattan, S.; Lewis, J.H., 2011:
Predicting and preventing acute drug-induced liver injury: what's new in 2010?

Harel, Z.; Chan, C.T., 2008:
Predicting and preventing acute kidney injury after cardiac surgery

Hottenrott, C., 2011:
Predicting and preventing anastomotic leakage after low anterior resection for rectal cancer

Purdey, S.; Huntley, A., 2014:
Predicting and preventing avoidable hospital admissions: a review

McIntyre, R.S.; Correll, C., 2015:
Predicting and preventing bipolar disorder: the need to fundamentally advance the strategic approach

Zambelli, A.; Della Porta, M.Giovanni.; Eleuteri, E.; D.G.uli, L.; Catalano, O.; Tondini, C.; Riccardi, A., 2011:
Predicting and preventing cardiotoxicity in the era of breast cancer targeted therapies. Novel molecular tools for clinical issues

Post, W.S., 2012:
Predicting and preventing cardiovascular disease in HIV-infected patients

Wicksell, R.K.; Olsson, G.L., 2010:
Predicting and preventing chronic postsurgical pain and disability

Dunlay, S.M.; Gersh, B.J., 2013:
Predicting and preventing heart failure rehospitalizations: is there a role for implantable device diagnostics?

Brown, K.R.; Dubovy, S.R.; Relhan, N.; Flynn, H.W., 2018:
Clinicopathologic Correlation of a Subretinal Proliferative Vitreoretinopathy Band in a Patient with Chronic Recurrent Retinal Detachment

Rabkin, S.W., 1985:
Predicting and preventing hypertension and associated cardiovascular disease

Pützer, B.M.; Steder, M.; Alla, V., 2011:
Predicting and preventing melanoma invasiveness: advances in clarifying E2F1 function

Dagnas, Séphane.; Membré, J-Marie., 2013:
Predicting and preventing mold spoilage of food products

Ziogas, D.; Ignatiadou, E.; Fatouros, M., 2008:
Predicting and preventing positive surgical margins and local failures in breast-conserving surgery

Papageorghiou, A.T., 2008:
Predicting and preventing pre-eclampsia-where to next?

de Lau, L.M.L.; den Hertog, H.M.; van den Herik, E.G.; Koudstaal, P.J., 2009:
Predicting and preventing stroke after transient ischemic attack

Estes, N.A.Mark., 2012:
Predicting and preventing sudden cardiac death

Anderson, B.; Sawyer, D.B., 2008:
Predicting and preventing the cardiotoxicity of cancer therapy

Hutchinson, M., 2009:
Predicting and preventing the future: actively managing multiple sclerosis

Wesselink, C.; Jansonius, N.M., 2014:
Predicting and preventing visual impairment and blindness by incorporating individual progression velocity in glaucoma care

D.V.co, L.; Iversen, L.; Sørensen, M.H.; Brandbyge, M.; Nygård, J.; Martinez, K.L.; Jensen, J.H., 2012:
Predicting and rationalizing the effect of surface charge distribution and orientation on nano-wire based FET bio-sensors

Day, A.J.; Bridger, R.S., 2012:
Predicting and reducing voluntary outflow in the Royal Navy

Morgenthaler, M.; Schweizer, E.; Hoffmann-Röder, A.; Benini, F.; Martin, R.E.; Jaeschke, G.; Wagner, Börn.; Fischer, H.; Bendels, S.; Zimmerli, D.; Schneider, J.; Diederich, Fçois.; Kansy, M.; Müller, K., 2007:
Predicting and tuning physicochemical properties in lead optimization: amine basicities

Stegle, O.; Payet, L.; Mergny, J-Louis.; MacKay, D.J.C.; Leon, J.Huppert., 2009:
Predicting and understanding the stability of G-quadruplexes

Chen, P-Yang.; Deane, C.M.; Reinert, G., 2008:
Predicting and validating protein interactions using network structure

Hamamura, K.; Chen, A.; Nishimura, A.; Tanjung, N.; Sudo, A.; Yokota, H., 2015:
Predicting and validating the pathway of Wnt3a-driven suppression of osteoclastogenesis

Gianaroli, L.; Magli, M.C.; Cavallini, G.; Crippa, A.; Capoti, A.; Resta, S.; Robles, F.; Ferraretti, A.P., 2010:
Predicting aneuploidy in human oocytes: key factors which affect the meiotic process

Prestigiacomo, C.J.; He, W.; Catrambone, J.; Chung, S.; Kasper, L.; Pasupuleti, L.; Mittal, N., 2008:
Predicting aneurysm rupture probabilities through the application of a computed tomography angiography-derived binary logistic regression model

Chan, Y.C.; Clough, R.E.; Taylor, P.R., 2011:
Predicting aneurysmal dilatation after type B aortic dissection

Trew, J.L.; Alden, L.E., 2012:
Predicting anger in social anxiety: The mediating role of rumination

Roberts, E.B.; Grayson, A.D.; Alahmar, A.E.; Andron, M.; Perry, R.; Stables, R.H., 2010:
Predicting angiographic outcome in contemporary percutaneous coronary intervention: a lesion-specific logistic model

Green, J.P.; Green, E.S., 2010:
Predicting animal attachment from hypnotizability, absorption, and dissociation scores

Smith, G.C.S., 2010:
Predicting antepartum stillbirth

Trompeter, A.J.; Gill, K.; Appleton, M.A.C.; Palmer, S.H., 2009:
Predicting anterior cruciate ligament integrity in patients with osteoarthritis

Moretti, E.; Oakman, C.; D.L.o, A., 2010:
Predicting anthracycline benefit: have we made any progress?

Ravichandran, V.; Prashantha Kumar, B.R.; Sankar, S.; Agrawal, R.K., 2008:
Predicting anti-HIV activity of 1,3,4-thiazolidinone derivatives: 3D-QSAR approach

Vaidya, A.; Jain, A.Kumar.; Kumar, P.; Kashaw, S.Kumar.; Agrawal, R.Kishore., 2012:
Predicting anti-cancer activity of quinoline derivatives: CoMFA and CoMSIA approach

Scott, S.Rich.; Rosen, L.H., 2016:
Predicting anti-fat attitudes: individual differences based on actual and perceived body size, weight importance, entity mindset, and ethnicity

Madaras-Kelly, K.J.; Remington, R.E.; Fan, V.S.; Sloan, K.L., 2012:
Predicting antibiotic resistance to community-acquired pneumonia antibiotics in culture-positive patients with healthcare-associated pneumonia

McCluskey, Sánín.M.; Knapp, C.W., 2011:
Predicting antibiotic resistance, not just for quinolones

Choi, Y.; Deane, C.M., 2012:
Predicting antibody complementarity determining region structures without classification

Hajisharifi, Z.; Piryaiee, M.; Mohammad Beigi, M.; Behbahani, M.; Mohabatkar, H., 2014:
Predicting anticancer peptides with Chou's pseudo amino acid composition and investigating their mutagenicity via Ames test

Klok, F.A.; Kooiman, J.; Huisman, M.V.; Konstantinides, S.; Lankeit, M., 2016:
Predicting anticoagulant-related bleeding in patients with venous thromboembolism: a clinically oriented review

Finney, O.C.; Danziger, S.A.; Molina, D.M.; Vignali, M.; Takagi, A.; Ji, M.; Stanisic, D.I.; Siba, P.M.; Liang, X.; Aitchison, J.D.; Mueller, I.; Gardner, M.J.; Wang, R., 2015:
Predicting antidisease immunity using proteome arrays and sera from children naturally exposed to malaria

Sillanpää, M.; Schmidt, D., 2011:
Predicting antiepileptic drug response in children with epilepsy

Liang, L.; Felgner, P.L., 2012:
Predicting antigenicity of proteins in a bacterial proteome; a protein microarray and naïve Bayes classification approach

González-Díaz, H.; Prado-Prado, F.; Ubeira, F.M., 2009:
Predicting antimicrobial drugs and targets with the MARCH-INSIDE approach

Amaral, Aé.C.; Silva, O.N.; Mundim, Nália.C.C.R.; de Carvalho, M.J.A.; Migliolo, L.; Leite, J.R.S.A.; Prates, M.V.; Bocca, Aélia.L.; Franco, Oávio.L.; Felipe, M.S.S., 2014:
Predicting antimicrobial peptides from eukaryotic genomes: in silico strategies to develop antibiotics

Gersing, K.; Burchett, B.; March, J.; Ostbye, T.; Krishnan, K.Ranga.Rama., 2007:
Predicting antipsychotic use in children

Garcia, F.; Alvarez, M.; Fox, Z.; Garcia-Diaz, A.; Guillot, V.; Johnson, M.; Chueca, N.; Phillips, A.; Hernández-Quero, Jé.; Geretti, A.Maria., 2011:
Predicting antiretroviral drug resistance from the latest or the cumulative genotype

Radman, A.; Gredičak, M.; Kopriva, I.; Jerić, I., 2012:
Predicting antitumor activity of peptides by consensus of regression models trained on a small data sample

Jácome, C.; Figueiredo, D.; Gabriel, R.; Cruz, J.; Marques, A., 2015:
Predicting anxiety and depression among family carers of people with Chronic Obstructive Pulmonary Disease

Weaving, J.; Orgeta, V.; Orrell, M.; Petrides, K.V., 2015:
Predicting anxiety in carers of people with dementia: the role of trait emotional intelligence

Berman, N.C.; Wheaton, M.G.; McGrath, P.; Abramowitz, J.S., 2010:
Predicting anxiety: the role of experiential avoidance and anxiety sensitivity

Carter, M.M.; Sbrocco, T.; Ayati, F., 2009:
Predicting anxious response to a social challenge and hyperventilation: comparison of the ASI and ASI-3

Thyrion, Cé.; Roll, J-Pierre., 2010:
Predicting any arm movement feedback to induce three-dimensional illusory movements in humans

Trimarchi, S.; Jonker, F.H.W.; van Bogerijen, G.H.W.; Tolenaar, J.L.; Moll, F.L.; Czerny, M.; Patel, H.J., 2014:
Predicting aortic enlargement in type B aortic dissection

Liao, B.; Jiang, J-Bao.; Zeng, Q-Guang.; Zhu, W., 2012:
Predicting apoptosis protein subcellular location with PseAAC by incorporating tripeptide composition

Travers, S.; Bertelsen, M.G.; Kucheryavskiy, S.V., 2014:
Predicting apple (cv. Elshof) postharvest dry matter and soluble solids content with near infrared spectroscopy

Ruml, M.; Milatović, D.; Vulić, T.; Vuković, A., 2011:
Predicting apricot phenology using meteorological data

Xiao, F.; Gulliver, J.S.; Simcik, M.F., 2014:
Predicting aqueous solubility of environmentally relevant compounds from molecular features: a simple but highly effective four-dimensional model based on Project to Latent Structures

Chu, K.A.; Yalkowsky, S.H., 2010:
Predicting aqueous solubility: the role of crystallinity

Walker, G.R.; Gilfedder, M.; Dawes, W.R.; Rassam, D.W., 2016:
Predicting aquifer response time for application in catchment modeling

Cox, V.; Patel, M.; Kim, J.; Liu, T.; Sivaraman, G.; Narayan, S.M., 2007:
Predicting arrhythmia-free survival using spectral and modified-moving average analyses of T-wave alternans

James, K.A.; Meliker, J.R.; Buttenfield, B.E.; Byers, T.; Zerbe, G.O.; Hokanson, J.E.; Marshall, J.A., 2015:
Predicting arsenic concentrations in groundwater of San Luis Valley, Colorado: implications for individual-level lifetime exposure assessment

Juhasz, A.L.; Weber, J.; Smith, E., 2012:
Predicting arsenic relative bioavailability in contaminated soils using meta analysis and relative bioavailability-bioaccessibility regression models

Raoufy, M.Reza.; Eftekhari, P.; Gharibzadeh, S.; Masjedi, M.Reza., 2011:
Predicting arterial blood gas values from venous samples in patients with acute exacerbation chronic obstructive pulmonary disease using artificial neural network

Bodanapally, U.K.; Krejza, J.; Saksobhavivat, N.; Jaffray, P.M.; Sliker, C.W.; Miller, L.A.; Shanmuganathan, K.; Dreizin, D., 2014:
Predicting arterial injuries after penetrating brain trauma based on scoring signs from emergency CT studies

Alty, S.R.; Angarita-Jaimes, N.; Millasseau, S.C.; Chowienczyk, P.J., 2007:
Predicting arterial stiffness from the digital volume pulse waveform

Berman, S.S.; Mendoza, B.; Westerband, A.; Quick, R.C., 2009:
Predicting arteriovenous fistula maturation with intraoperative blood flow measurements

de Rooy, D.P.C.; van der Linden, M.P.M.; Knevel, R.; Huizinga, T.W.J.; van der Helm-van Mil, A.H.M., 2011:
Predicting arthritis outcomes--what can be learned from the Leiden Early Arthritis Clinic?

Power, M.L.; Hamdy, S.; Goulermas, J.Y.; Tyrrell, P.J.; Turnbull, I.; Thompson, D.G., 2010:
Predicting aspiration after hemispheric stroke from timing measures of oropharyngeal bolus flow and laryngeal closure

Smith Hammond, C.A.; Goldstein, L.B.; Horner, R.D.; Ying, J.; Gray, L.; Gonzalez-Rothi, L.; Bolser, D.C., 2008 :
Predicting aspiration in patients with ischemic stroke: comparison of clinical signs and aerodynamic measures of voluntary cough

He, Y.; Wang, J.; Lek-Ang, S.; Lek, S., 2010:
Predicting assemblages and species richness of endemic fish in the upper Yangtze River

de Groot, E.P.; Brand, P.L.P.; Duiverman, E.J., 2009:
Predicting asthma by means of wheezing at a young age is not possible

Frey, U., 2007:
Predicting asthma control and exacerbations: chronic asthma as a complex dynamic model

Jones, C.A.; Bender, B.G.; Haselkorn, T.; Fish, J.E.; Mink, D.R.; Peters, S.P.; Weiss, S.T., 2009:
Predicting asthma control using patient attitudes toward medical care: the REACT score

Ritz, T.; Bobb, C.; Griffiths, C., 2015:
Predicting asthma control: the role of psychological triggers

Forno, E.; Celedón, J.C., 2012:
Predicting asthma exacerbations in children

Finkelstein, J.; Wood, J., 2014:
Predicting asthma exacerbations using artificial intelligence

Fuhlbrigge, A.L., 2011:
Predicting asthma exacerbations: peak expiratory flow revisited

van der Mark, L.B.; van Wonderen, K.E.; Mohrs, J.; van Aalderen, W.M.C.; ter Riet, G.; Bindels, P.J.E., 2014:
Predicting asthma in preschool children at high risk presenting in primary care: development of a clinical asthma prediction score

Hafkamp-de Groen, E.; Lingsma, H.F.; Caudri, D.; Levie, D.; Wijga, A.; Koppelman, G.H.; Duijts, L.; Jaddoe, V.W.V.; Smit, Hëtte.A.; Kerkhof, M.; Moll, Hëtte.A.; Hofman, A.; Steyerberg, E.W.; de Jongste, J.C.; Raat, H., 2014:
Predicting asthma in preschool children with asthma-like symptoms: validating and updating the PIAMA risk score

Stanford, R.H.; Shah, M.B.; D'Souza, A.O.; Schatz, M., 2013:
Predicting asthma outcomes in commercially insured and Medicaid populations?

Janssens, T.; Verleden, G.; D.P.uter, S.; Petersen, S.; Van den Bergh, O., 2012:
Predicting asthma treatment outcome at diagnosis: the role of symptom perception during a histamine challenge test

Keeley, D.W.; Plummer, H.A.; Oliver, G.D., 2011:
Predicting asymmetrical lower extremity strength deficits in college-aged men and women using common horizontal and vertical power field tests: a possible screening mechanism

Calvet, D.; Song, D.; Yoo, J.; Turc, G.; Sablayrolles, J-Louis.; Choi, B.Wook.; Heo, J.Hoe.; Mas, J-Louis., 2014:
Predicting asymptomatic coronary artery disease in patients with ischemic stroke and transient ischemic attack: the PRECORIS score

Gompelmann, D.; Eberhardt, R.; Michaud, G.; Ernst, A.; Herth, F.J.F., 2011:
Predicting atelectasis by assessment of collateral ventilation prior to endobronchial lung volume reduction: a feasibility study

Kajbafnezhad, H.; Ahadi, H.; Heidarie, A.; Askari, P.; Enayati, M., 2013:
Predicting athletic success motivation using mental skin and emotional intelligence and its components in male athletes

Tuszynska, I.; Bujnicki, J.M., 2010:
Predicting atomic details of the unfolding pathway for YibK, a knotted protein from the SPOUT superfamily

Arnar, D.O.; Holm, H.; Gudbjartsson, D.F., 2009 :
Predicting atrial fibrillation

Karnik, S.; Tan, S.Lam.; Berg, B.; Glurich, I.; Zhang, J.; Vidaillet, H.J.; Page, C.David.; Chowdhary, R., 2013:
Predicting atrial fibrillation and flutter using electronic health records

Bai, B.; Wang, Y.; Yang, C., 2014:
Predicting atrial fibrillation inducibility in a canine model by multi-threshold spectra of the recurrence complex network

Masson, S.; Aleksova, A.; Favero, C.; Staszewsky, L.; Bernardinangeli, M.; Belvito, C.; Cioffi, G.; Sinagra, G.; Mazzone, C.; Bertocchi, F.; Vago, T.; Peri, G.; Cuccovillo, I.; Masuda, N.; Barlera, S.; Mantovani, A.; Maggioni, A.P.; Franzosi, M.Grazia.; Disertori, M.; Latini, R.; Rossi, M.G.; Fazi, A.; Pierfelice, O.; Gavazzi, A.; Taddei, F.; Rigatelli, G.; Boni, S.; Trappolin, R.; Muscella, A.; Vetrano, A.; Gulizia, M.; Francese, G.M.; Perticone, F.; Severini, D.; Pirelli, S.; Spotti, A.; Maria, 2011:
Predicting atrial fibrillation recurrence with circulating inflammatory markers in patients in sinus rhythm at high risk for atrial fibrillation: data from the GISSI atrial fibrillation trial

John, R.M.; Stevenson, W.G., 2016:
Predicting atrial fibrillation: can we shape the future?

White, K.M.; Thomas, I.; Johnston, K.L.; Hyde, M.K., 2008:
Predicting attendance at peer-assisted study sessions for statistics: role identity and the theory of planned behavior

Klein, R.; Knäuper, Bärbel., 2010:
Predicting attention and avoidance: when do avoiders attend?

Harvey, E.A.; Youngwirth, S.D.; Thakar, D.A.; Errazuriz, P.A., 2009:
Predicting attention-deficit/hyperactivity disorder and oppositional defiant disorder from preschool diagnostic assessments

Morena, M.A.; Franke, J.E., 2012:
Predicting attenuant and resonant 2-cycles in periodically forced discrete-time two-species population models

Jalleh, G.; Donovan, R.J.; Jobling, I., 2015:
Predicting attitude towards performance enhancing substance use: a comprehensive test of the Sport Drug Control Model with elite Australian athletes

Storvoll, E.E.; Moan, I.Synnøve.; Rise, J., 2016:
Predicting attitudes toward a restrictive alcohol policy: Using a model of distal and proximal predictors

Earl, B.R.; Chertoff, M.E., 2010:
Predicting auditory nerve survival using the compound action potential

Veness, C.; Prior, M.; Eadie, P.; Bavin, E.; Reilly, S., 2015:
Predicting autism diagnosis by 7 years of age using parent report of infant social communication skills

Tzioufas, A.G.; Moutsopoulos, H.M., 2007:
Predicting autoimmune congenital heart block: is it feasible and how?

Azzopardi, L.; Thompson, S.A.J.; Harding, K.E.; Cossburn, M.; Robertson, N.; Compston, A.; Coles, A.J.; Jones, J.L., 2014:
Predicting autoimmunity after alemtuzumab treatment of multiple sclerosis

de Bruijn, G-Jan.; Gardner, B.; van Osch, L.; Sniehotta, F.F., 2015:
Predicting automaticity in exercise behaviour: the role of perceived behavioural control, affect, intention, action planning, and behaviour

Bryantsev, V.S.; Faglioni, F., 2012:
Predicting autoxidation stability of ether- and amide-based electrolyte solvents for Li-air batteries

Steenland, H.W.; Li, X-Yao.; Zhuo, M., 2012:
Predicting aversive events and terminating fear in the mouse anterior cingulate cortex during trace fear conditioning

Szabo, J.K.; Davy, P.J.; Hooper, M.J.; Astheimer, L.B., 2010:
Predicting avian distributions to evaluate spatiotemporal overlap with locust control operations in eastern Australia

Kolarik, D.; Pecha, V.; Skovajsova, M.; Zahumensky, J.; Trnkova, M.; Petruzelka, L.; Halaska, M.; Sottner, O.; Otcenasek, M.; Kolarova, H., 2013:
Predicting axillary sentinel node status in patients with primary breast cancer

Griffith, T.S., 2007:
Predicting bacillus Calmette-Guerin immunotherapy effectiveness

Lyons, J.; Dehzangi, A.; Heffernan, R.; Sharma, A.; Paliwal, K.; Sattar, A.; Zhou, Y.; Yang, Y., 2015:
Predicting backbone Cα angles and dihedrals from protein sequences by stacked sparse auto-encoder deep neural network

Lipsky, B.A.; Kollef, M.H.; Miller, L.G.; Sun, X.; Johannes, R.S.; Tabak, Y.P., 2010:
Predicting bacteremia among patients hospitalized for skin and skin-structure infections: derivation and validation of a risk score

Komatsu, T.; Onda, T.; Murayama, G.; Yamanouchi, M.; Inukai, M.; Sakai, A.; Kikuta, M.; Branch, J.; Aoki, M.; Tierney, L.M.; Inoue, K., 2013:
Predicting bacteremia based on nurse-assessed food consumption at the time of blood culture

Albrich, W.C.; Mueller, B., 2011:
Predicting bacteremia by procalcitonin levels in patients evaluated for sepsis in the emergency department

Agyeman, P.; Aebi, C.; Hirt, A.; Niggli, F.K.; Nadal, D.; Simon, A.; Ozsahin, H.; Kontny, U.; Kühne, T.; Beck Popovic, M.; Leibundgut, K.; Bodmer, N.; Ammann, R.A., 2011:
Predicting bacteremia in children with cancer and fever in chemotherapy-induced neutropenia: results of the prospective multicenter SPOG 2003 FN study

Perelló, R.; Miró, O.; Marcos, Mía.Angeles.; Almela, M.; Bragulat, E.; Sánchez, M.; Agustí, C.; Miro, Jé.M.; Moreno, Aón., 2010 :
Predicting bacteremic pneumonia in HIV-1-infected patients consulting the ED

Ning, L.W.; Lin, H.; Ding, H.; Huang, J.; Rao, N.; Guo, F.B., 2015:
Predicting bacterial essential genes using only sequence composition information

Stocks, G.W.; Self, S.D.; Thompson, B.; Adame, X.A.; O'Connor, D.P., 2010:
Predicting bacterial populations based on airborne particulates: a study performed in nonlaminar flow operating rooms during joint arthroplasty surgery

Firsov, A.A.; Portnoy, Y.A.; Strukova, E.N.; Shlykova, D.S.; Zinner, S.H., 2015:
Predicting bacterial resistance using the time inside the mutant selection window: possibilities and limitations

Wong, J.Eiin.; Poh, B.Koon.; Nik Shanita, S.; Izham, M.Mohamad.; Chan, K.Quin.; Tai, M.De.; Ng, W.Wei.; Ismail, M.Noor., 2013:
Predicting basal metabolic rates in Malaysian adult elite athletes

Wald, R., 2012:
Predicting baseline creatinine in hospitalized patients

Matthews, G.; Warm, J.S.; Shaw, T.H.; Finomore, V.S., 2015:
Predicting battlefield vigilance: a multivariate approach to assessment of attentional resources

Forsythe, A.; Nadal, M.; Sheehy, N.; Cela-Conde, C.J.; Sawey, M., 2011:
Predicting beauty: fractal dimension and visual complexity in art

Navajas, E.A.; Richardson, R.I.; Fisher, A.V.; Hyslop, J.J.; Ross, D.W.; Prieto, N.; Simm, G.; Roehe, R., 2010:
Predicting beef carcass composition using tissue weights of a primal cut assessed by computed tomography

Prieto, N.; Navajas, E.A.; Richardson, R.I.; Ross, D.W.; Hyslop, J.J.; Simm, G.; Roehe, R., 2011:
Predicting beef cuts composition, fatty acids and meat quality characteristics by spiral computed tomography

Rust, S.R.; Price, D.M.; Subbiah, J.; Kranzler, G.; Hilton, G.G.; Vanoverbeke, D.L.; Morgan, J.B., 2007:
Predicting beef tenderness using near-infrared spectroscopy

Trawalter, S.; Richeson, J.A.; Shelton, J.Nicole., 2010:
Predicting behavior during interracial interactions: a stress and coping approach

Mellers, B.A.; Haselhuhn, M.P.; Tetlock, P.E.; Silva, Jé.C.; Isen, A.M., 2011:
Predicting behavior in economic games by looking through the eyes of the players

Ingham, S.C.; Vang, S.; Levey, B.; Fahey, L.; Norback, J.P.; Fanslau, M.A.; Senecal, A.G.; Burnham, G.M.; Ingham, B.H., 2010:
Predicting behavior of Staphylococcus aureus, salmonella serovars, and Escherichia coli O157: H7 in pork products during single and repeated temperature abuse periods

Doyle, L.A.; Fletcher, C.D.M., 2014:
Predicting behavior of solitary fibrous tumor: are we getting closer to more accurate risk assessment?

Barker, D.H.; Quittner, A.L.; Fink, N.E.; Eisenberg, L.S.; Tobey, E.A.; Niparko, J.K.; Eisenberg, L.; Luxford, W.; Johnson, K.; Martinez, A.; DesJardin, J.; Visser-Dumon, L.; Ambrose, S.; Stika, C.; Gillinger, M.; Niparko, J.; Chinnici, J.; Francis, H.; Bowditch, S.; Yeagle, J.; Carver, C.; Marlowe, A.; Gregg, A.; Gross, J.; Ostrander, R.; Mellon, N.; Mertes, J.; Kane, M.O'Leary.; Hodges, A.; Balkany, T.; Lopez, A.; Goodwin, L.; Zwolan, T.; O'Sullivan, M.Beth.; Vereb, A.; Arnedt, C.; T, 2009:
Predicting behavior problems in deaf and hearing children: the influences of language, attention, and parent-child communication

Johnson, C.P.; Juranek, J.; Kramer, L.A.; Prasad, M.R.; Swank, P.R.; Ewing-Cobbs, L., 2013:
Predicting behavioral deficits in pediatric traumatic brain injury through uncinate fasciculus integrity

Scholz, U.; Keller, R.; Perren, S., 2010:
Predicting behavioral intentions and physical exercise: a test of the health action process approach at the intrapersonal level

Dolson, E.P.; Conklin, H.M.; Li, C.; Xiong, X.; Merchant, T.E., 2009:
Predicting behavioral problems in craniopharyngioma survivors after conformal radiation therapy

Spence, A.; Townsend, E., 2007:
Predicting behaviour towards genetically modified food using implicit and explicit attitudes

Norman, R.M.G.; Sorrentino, R.M.; Windell, D.; Ye, Y.; Szeto, A.C.H.; Manchanda, R., 2010:
Predicting behavioural intentions to those with mental illness: the role of attitude specificity and norms

O'Shea, J.; Boudrias, M-Hélène.; Stagg, C.Jane.; Bachtiar, V.; Kischka, U.; Blicher, J.Udby.; Johansen-Berg, H., 2014:
Predicting behavioural response to TDCS in chronic motor stroke

Soliman, E.Z., 2008:
Predicting benefit for implantable cardioverter-defibrillator use

Exner, D.V., 2012:
Predicting benefit from CRT when is it too little, too late?

Freedman, O.; Amir, E.; Dranitsaris, G.; Napolskikh, J.; Kumar, R.; Fralick, M.; Chia, S.; Petrella, T.; Dent, S.; Tonkin, K.; Ahmad, I.; Rayson, D.; Clemons, M., 2009:
Predicting benefit from fulvestrant in pretreated metastatic breast cancer patients

Gandhi, M.D.; Kaklamani, V.G., 2015:
Predicting benefit from imatinib: are we close?

Buckley, O.; D.C.rli, M., 2011:
Predicting benefit from revascularization in patients with ischemic heart failure: imaging of myocardial ischemia and viability

Stewart, R.A.H., 2009:
Predicting benefit from statins by C-reactive protein, LDL-cholesterol or absolute cardiovascular risk

Ebell, M.H., 2009:
Predicting benefit of spinal manipulation for low back pain

van Vugt, S.F.; Butler, C.C.; Hood, K.; Kelly, M.J.; Coenen, S.; Goossens, H.; Little, P.; Verheij, T.J., 2012:
Predicting benign course and prolonged illness in lower respiratory tract infections: a 13 European country study

Kountouris, P.; Hirst, J.D., 2010:
Predicting beta-turns and their types using predicted backbone dihedral angles and secondary structures

Elbashir, M.; Wang, J.; Wu, F-Xiang.; Wang, L., 2014:
Predicting beta-turns in proteins using support vector machines with fractional polynomials

Scarpino, S.E.; Lawrence, F.R.; Davison, M.D.; Hammer, C.S., 2011:
Predicting bilingual Spanish-English children's phonological awareness abilities from their preschool English and Spanish oral language

Ahlfors, C.E., 2010:
Predicting bilirubin neurotoxicity in jaundiced newborns

Jerger, J., 2011:
Predicting binaural interference

König, G.; Brooks, B.R., 2013:
Predicting binding affinities of host-guest systems in the SAMPL3 blind challenge: the performance of relative free energy calculations

Fourches, D.; Muratov, E.; Ding, F.; Dokholyan, N.V.; Tropsha, A., 2014:
Predicting binding affinity of CSAR ligands using both structure-based and ligand-based approaches

Ming, D.; Wall, M.E., 2012:
Predicting binding sites by analyzing allosteric effects

Dolghih, E.; Bryant, C.; Renslo, A.R.; Jacobson, M.P., 2011:
Predicting binding to p-glycoprotein by flexible receptor docking

Khan, W.; Duffy, F.; Pollastri, G.; Shields, D.C.; Mooney, C., 2014:
Predicting binding within disordered protein regions to structurally characterised peptide-binding domains

Froehner, S.; Maceno, M.; Machado, K.Scurupa., 2011:
Predicting bioaccumulation of PAHs in the trophic chain in the estuary region of Paranagua, Brazil

O'Donnell, M.D., 2011:
Predicting bioactive glass properties from the molecular chemical composition: glass transition temperature

Chen, W-Yu.; Liao, C-Min.; Jou, L-John.; Jau, S-Feng., 2010:
Predicting bioavailability and bioaccumulation of arsenic by freshwater clam Corbicula fluminea using valve daily activity

Golui, D.; Datta, S.P.; Rattan, R.K.; Dwivedi, B.S.; Meena, M.C., 2015:
Predicting bioavailability of metals from sludge-amended soils

Soto, D.E.; Andridge, R.R.; Taylor, J.M.G.; McLaughlin, P.W.; Sandler, H.M.; Pan, C.C., 2008:
Predicting biochemical failure and overall survival through intratherapy PSA changes during definitive external beam radiotherapy

Williams, S.B.; Hu, J.C., 2015:
Predicting biochemical recurrence following salvage radiotherapy: applying lessons learned from primary radiotherapy

von Bodman, C.; Godoy, G.; Chade, D.C.; Cronin, A.; Tafe, L.J.; Fine, S.W.; Laudone, V.; Scardino, P.T.; Eastham, J.A., 2010:
Predicting biochemical recurrence-free survival for patients with positive pelvic lymph nodes at radical prostatectomy

Mazzola, C.R.; Katz, D.J.; Loghmanieh, N.; Nelson, C.J.; Mulhall, J.P., 2015:
Predicting biochemical response to clomiphene citrate in men with hypogonadism

Zelefsky, M.J.; Chou, J.F.; Pei, X.; Yamada, Y.; Kollmeier, M.; Cox, B.; Zhang, Z.; Schechter, M.; Cohen, G'ad.N.; Zaider, M., 2012:
Predicting biochemical tumor control after brachytherapy for clinically localized prostate cancer: The Memorial Sloan-Kettering Cancer Center experience

Mason, I.G., 2008:
Predicting biodegradable volatile solids degradation profiles in the composting process

Wicker, Jörg.; Fenner, K.; Ellis, L.; Wackett, L.; Kramer, S., 2010:
Predicting biodegradation products and pathways: a hybrid knowledge- and machine learning-based approach

Dutt, R.; Madan, A.K., 2013:
Predicting biological activity: computational approach using novel distance based molecular descriptors

Hu, L-Le.; Chen, C.; Huang, T.; Cai, Y-Dong.; Chou, K-Chen., 2012:
Predicting biological functions of compounds based on chemical-chemical interactions

Doll, J.C., 2012:
Predicting biological impairment from habitat assessments

Sheppard, A.W., 1992:
Predicting biological weed control

Kou, P.Meng.; Pallassana, N.; Bowden, R.; Cunningham, B.; Joy, A.; Kohn, J.; Babensee, J.E., 2012:
Predicting biomaterial property-dendritic cell phenotype relationships from the multivariate analysis of responses to polymethacrylates

Goodwin, J.Caleb.; Johnson, T.R.; Cohen, T.; Herskovic, J.R.; Bernstam, E.V., 2012:
Predicting biomedical document access as a function of past use

Caso, J.R.; Tsivian, M.; Mouraviev, V.; Polascik, T.J., 2012:
Predicting biopsy-proven prostate cancer recurrence following cryosurgery

Parmenter, J.; Mitchell, C.; Keen, J.; Oliver, P.; Rowse, G.; Neligan, I.; Keil, C.; Mathers, N., 2014:
Predicting biopsychosocial outcomes for heroin users in primary care treatment: a prospective longitudinal cohort study

Malhi, G.S.; Bargh, D.M.; Coulston, C.M.; Das, P.; Berk, M., 2015:
Predicting bipolar disorder on the basis of phenomenology: implications for prevention and early intervention

Geoffroy, P.A.; Leboyer, M.; Scott, J., 2015:
Predicting bipolar disorder: what can we learn from prospective cohort studies?

Smith, T.B.; Harrigan, R.J.; Kirschel, A.N.G.; Buermann, W.; Saatchi, S.; Blumstein, D.T.; de Kort, S.R.; Slabbekoorn, H., 2013:
Predicting bird song from space

Cannella, D.; Auerbach, M.; Lobel, M., 2014:
Predicting birth outcomes: together, mother and health care provider know best

Möst, L.; Schmid, M.; Faschingbauer, F.; Hothorn, T., 2014:
Predicting birth weight with conditionally linear transformation models

Kamminga, K.L.; Herbert, D.Ames.; Kuhar, T.P.; Brewster, C.C., 2010:
Predicting black light trap catch and flight activity of Acrosternum hilare (Hemiptera: Pentatomidae) adults

Rafiee, R.M.; Melamed, S.; Chao, J., 2012:
Predicting black triangles. Part II: interdental width

Rosenstein, R.; DiMaggio, E., 2011:
Predicting bleeding risk in anticoagulated patients with atrial fibrillation

Masser, B.M.; White, K.M.; Hyde, M.K.; Terry, D.J.; Robinson, N.G., 2008:
Predicting blood donation intentions and behavior among Australian blood donors: testing an extended theory of planned behavior model

van Dongen, A.; Ruiter, R.; Abraham, C.; Veldhuizen, I., 2014:
Predicting blood donation maintenance: the importance of planning future donations

Thompson, P.A.; May, D.; Choong, P.F.; Tacey, M.; Liew, D.; Cole-Sinclair, M.F., 2014:
Predicting blood loss and transfusion requirement in patients undergoing surgery for musculoskeletal tumors

Magnussen, R.A.; Tressler, M.A.; Obremskey, W.T.; Kregor, P.J., 2007:
Predicting blood loss in isolated pelvic and acetabular high-energy trauma

Beck, A.W.; Nolan, B.W.; D.M.rtino, R.; Yuo, T.H.; Tanski, W.J.; Walsh, D.B.; Powell, R.P.; Cronenwett, J.L., 2010:
Predicting blood pressure response after renal artery stenting

Fu, X-Chun.; Wang, G-Ping.; Shan, H-Li.; Liang, W-Quan.; Gao, J-Qing., 2008:
Predicting blood-brain barrier penetration from molecular weight and number of polar atoms

Leth, R.Andersen.; Forman, B.Elisabeth.; Kristensen, B., 2013:
Predicting bloodstream infection via systemic inflammatory response syndrome or biochemistry

Faridi, A.; Sakomura, N.K.; Golian, A.; Marcato, S.M., 2013:
Predicting body and carcass characteristics of 2 broiler chicken strains using support vector regression and neural network models

Loenneke, J.P.; Hirt, K.M.; Wilson, J.M.; Barnes, J.T.; Pujol, T.J., 2013:
Predicting body composition in college students using the womersley and durnin body mass index equation

Kupusinac, A.; Stokić, E.; Doroslovački, R., 2014:
Predicting body fat percentage based on gender, age and BMI by using artificial neural networks

Goodarzi, M.; Freitas, M.P., 2008:
Predicting boiling points of aliphatic alcohols through multivariate image analysis applied to quantitative structure-property relationships

Hennessey, D.C.; Sheppard, B.J.H.; Mackenzie, D.E.C.K.; Pearson, J.K., 2014:
Predicting bond strength from a single Hartree-Fock ground state using the localized pair model

Eser, A.; Tonuk, E.; Akca, K.; Dard, M.M.; Cehreli, M.Cavit., 2014:
Predicting bone remodeling around tissue- and bone-level dental implants used in reduced bone width

Tavakkoli Avval, P.; Klika, Václav.; Bougherara, H., 2015:
Predicting bone remodeling in response to total hip arthroplasty: computational study using mechanobiochemical model

Lai, M.H.Y.; Luk, W.Hang.; Chan, J.C.S., 2011:
Predicting bone scan findings using sPSA in patients newly diagnosed of prostate cancer: feasibility in Asian population

Fossati, A.; Borroni, S.; Feeney, J.; Maffei, C., 2012:
Predicting borderline personality disorder features from personality traits, identity orientation, and attachment styles in Italian nonclinical adults: issues of consistency across age ranges

Vaillancourt, T.; Brittain, H.L.; McDougall, P.; Krygsman, A.; Boylan, K.; Duku, E.; Hymel, S., 2015:
Predicting borderline personality disorder symptoms in adolescents from childhood physical and relational aggression, depression, and attention-deficit/hyperactivity disorder

Bujard, A.; Sol, M.; Carrupt, P-Alain.; Martel, S., 2015:
Predicting both passive intestinal absorption and the dissociation constant toward albumin using the PAMPA technique

Rutten, M.J.M.; Bovenhuis, H.; Hettinga, K.A.; van Valenberg, H.J.F.; van Arendonk, J.A.M., 2010:
Predicting bovine milk fat composition using infrared spectroscopy based on milk samples collected in winter and summer

Derado, G.; Bowman, F.Dubois.; Zhang, L.; Weiner, M.; Aisen, P.; Weiner, M.; Aisen, P.; Petersen, R.; Jack, C.R.; Jagust, W.; Trojanowki, J.Q.; Toga, A.W.; Beckett, L.; Green, R.C.; Saykin, A.J.; Morris, J.; Liu, E.; Green, R.C.; Montine, T.; Petersen, R.; Aisen, P.; Gamst, A.; Thomas, R.G.; Donohue, M.; Walter, S.; Gessert, D.; Sather, T.; Beckett, L.; Harvey, D.; Gamst, A.; Donohue, M.; Kornak, J.; Jack, C.R.; Dale, A.; Bernstein, M.; Felmlee, J.; Fox, N.; Thompson, P.; Schuff, N.; Alexander,, 2014:
Predicting brain activity using a Bayesian spatial model

Bechmann, T.; Madsen, J.Skov.; Brandslund, I.; Lund, E.Dalsgaard.; Ormstrup, T.; Jakobsen, E.Hugger.; Jylling, A.Marie.Bak.; Steffensen, K.Dahl.; Jakobsen, A., 2013:
Predicting brain metastases of breast cancer based on serum S100B and serum HER2

Behroozi, M.; Daliri, M.Reza., 2015:
Predicting brain states associated with object categories from fMRI data

Shinohara, R.T.; Goldsmith, J.; Mateen, F.J.; Crainiceanu, C.; Reich, D.S., 2013:
Predicting breakdown of the blood-brain barrier in multiple sclerosis without contrast agents

Wong, C.L.; Mullan, B.A., 2008:
Predicting breakfast consumption: an application of the theory of planned behaviour and the investigation of past behaviour and executive function

Todd, L.; Harvey, E.; Hoffman-Goetz, L., 2011:
Predicting breast and colon cancer screening among English-as-a-second-language older Chinese immigrant women to Canada

Nurkalem, Z.; Sahin, S.; Uslu, N.; Emre, A.; Alper, A.Taha.; Gorgulu, S.; Yardi, F.; Eren, M., 2007:
Predicting breast attenuation in patients undergoing myocardial perfusion scintigraphy: a digital x-ray study

Cheng, J.; Greshock, J.; Painter, J.; Lin, X.; Lee, K.; Zheng, S.; Menius, A., 2012:
Predicting breast cancer chemotherapeutic response using a novel tool for microarray data analysis

Mattioli, E.; Giordano, A.; Pentimalli, F., 2014:
Predicting breast cancer outcome: traditional prognosticators still on center stage

Li, S.; Yu, K-Da.; Fan, L.; Hou, Y-Feng.; Shao, Z-Ming., 2012:
Predicting breast cancer recurrence following breast-conserving therapy: a single-institution analysis consisting of 764 Chinese breast cancer cases

Stone, J.; Ding, J.; Warren, R.M.L.; Duffy, S.W., 2011:
Predicting breast cancer risk using mammographic density measurements from both mammogram sides and views

Anderson, E.; Berg, J.; Black, R.; Bradshaw, N.; Campbell, J.; Cetnarskyj, R.; Drummond, S.; Davidson, R.; Dunlop, J.; Fordyce, A.; Gibbons, B.; Goudie, D.; Gregory, H.; Hanning, K.; Holloway, S.; Longmuir, M.; McLeish, L.; Murday, V.; Miedzybrodska, Z.; Nicholson, D.; Pearson, P.; Porteous, M.; Reis, M.; Slater, S.; Smith, K.; Smyth, E.; Snadden, L.; Steel, M.; Stirling, D.; Watt, C.; Whyte, C.; Young, D., 2008:
Predicting breast cancer risk: implications of a "weak" family history

Khan, M.Umer.; Choi, J.Pill.; Shin, H.; Kim, M., 2009:
Predicting breast cancer survivability using fuzzy decision trees for personalized healthcare

Murray, J.D.; Elwood, E.T.; Barrick, R.; Feng, J., 2008 :
Predicting breast reduction weight using the mass of breast ptosis

Baskaran, V.; Guergachi, A.; Bali, R.K.; Naguib, R.N.G., 2011:
Predicting breast screening attendance using machine learning techniques

Jiang, S.; Pogue, B.W.; Kaufman, P.A.; Gui, J.; Jermyn, M.; Frazee, T.E.; Poplack, S.P.; DiFlorio-Alexander, R.; Wells, W.A.; Paulsen, K.D., 2015:
Predicting breast tumor response to neoadjuvant chemotherapy with diffuse optical spectroscopic tomography prior to treatment

Gill, S.L.; Reifsnider, E.; Lucke, J.F.; Mann, A.R., 2007:
Predicting breast-feeding attrition: adapting the breast-feeding attrition prediction tool

Ho, Y-Ju.; McGrath, J.M., 2012:
Predicting breastfeeding duration related to maternal attitudes in a taiwanese sample

Kuehn, L.A.; Keele, J.W.; Bennett, G.L.; McDaneld, T.G.; Smith, T.P.L.; Snelling, W.M.; Sonstegard, T.S.; Thallman, R.M., 2011:
Predicting breed composition using breed frequencies of 50,000 markers from the US Meat Animal Research Center 2,000 Bull Project

Bartelt, P.E.; Gallant, A.L.; Klaver, R.W.; Wright, C.K.; Patla, D.A.; Peterson, C.R., 2011:
Predicting breeding habitat for amphibians: a spatiotemporal analysis across Yellowstone National Park

Raub, C.B.; Putnam, A.J.; Tromberg, B.J.; George, S.C., 2011:
Predicting bulk mechanical properties of cellularized collagen gels using multiphoton microscopy

Roe, J.H.; Morreale, S.J.; Paladino, F.V.; Shillinger, G.L.; Benson, S.R.; Eckert, S.A.; Bailey, H.; Tomillo, P.Santidrián.; Bograd, S.J.; Eguchi, T.; Dutton, P.H.; Seminoff, J.A.; Block, B.A.; Spotila, J.R., 2014:
Predicting bycatch hotspots for endangered leatherback turtles on longlines in the Pacific Ocean

Lavoie, M.; Campbell, P.G.C.; Fortin, C., 2014:
Predicting cadmium accumulation and toxicity in a green alga in the presence of varying essential element concentrations using a biotic ligand model

Mottaghitalab, M.; Faridi, A.; Darmani-Kuhi, H.; France, J.; Ahmadi, H., 2010:
Predicting caloric and feed efficiency in turkeys using the group method of data handling-type neural networks

Guglielmetti, G.B.; Danilovic, A.; Torricelli, F.C.M.; Coelho, R.F.; Mazzucchi, E.; Srougi, M., 2014:
Predicting calyceal access for percutaneous nephrolithotomy with computed tomography multiplanar reconstruction

Reynolds, K.J.; Cleek, T.M.; Mohtar, A.A.; Hearn, T.C., 2013:
Predicting cancellous bone failure during screw insertion

Pritchard, J.R.; Bruno, P.M.; Hemann, M.T.; Lauffenburger, D.A., 2014:
Predicting cancer drug mechanisms of action using molecular network signatures

Zickmund, S.L.; Yang, S.; Mulvey, E.P.; Bost, J.E.; Shinkunas, L.A.; LaBrecque, D.R., 2014:
Predicting cancer mortality: Developing a new cancer care variable using mixed methods and the quasi-statistical approach

Doren, E.; Hulvat, M.; Norton, J.; Rajan, P.; Sarker, S.; Aranha, G.; Yao, K., 2008:
Predicting cancer on excision of atypical ductal hyperplasia

Grande, G.; Arnott, J.; Brundle, C.; Pilling, M., 2016:
Predicting cancer patients' participation in support groups: a longitudinal study

Das, J.; Gayvert, K.M.; Yu, H., 2014:
Predicting cancer prognosis using functional genomics data sets

Rabin, B.A.; Gaglio, B.; Sanders, T.; Nekhlyudov, L.; Dearing, J.W.; Bull, S.; Glasgow, R.E.; Marcus, A., 2014:
Predicting cancer prognosis using interactive online tools: a systematic review and implications for cancer care providers

Hovick, S.R.; Liang, M-Ching.; Kahlor, L., 2015:
Predicting cancer risk knowledge and information seeking: the role of social and cognitive factors

Wu, T-H.; Lin, W-C.; Chen, W-K.; Chang, Y-C.; Hwang, J-J., 2015:
Predicting cancer risks from dental computed tomography

Zigeuner, R.; Pummer, K., 2009:
Predicting cancer-control outcomes in patients after nephron sparing surgery

Isbarn, H.; Karakiewicz, P.I., 2009:
Predicting cancer-control outcomes in patients with renal cell carcinoma

Wang, H., 2015:
Predicting cancer-related MiRNAs using expression profiles in tumor tissue

Jerby-Arnon, L.; Pfetzer, N.; Waldman, Y.Y.; McGarry, L.; James, D.; Shanks, E.; Seashore-Ludlow, B.; Weinstock, A.; Geiger, T.; Clemons, P.A.; Gottlieb, E.; Ruppin, E., 2015:
Predicting cancer-specific vulnerability via data-driven detection of synthetic lethality

Zhang, L.; Li, X.; Tai, J.; Li, W.; Chen, L., 2013:
Predicting candidate genes based on combined network topological features: a case study in coronary artery disease

Blankers, M.; Frijns, T.; Belackova, V.; Rossi, C.; Svensson, B.; Trautmann, F.; van Laar, M., 2015:
Predicting cannabis abuse screening test (CAST) scores: a recursive partitioning analysis using survey data from Czech Republic, Italy, the Netherlands and Sweden

D.P.ula, N.F.; Tedeschi, L.O.; Paulino, M.F.; Fernandes, H.J.; Fonseca, M.A., 2014:
Predicting carcass and body fat composition using biometric measurements of grazing beef cattle

Yuan, J.; Pu, Y.; Yin, L., 2012:
Predicting carcinogenicity and understanding the carcinogenic mechanism of N-nitroso compounds using a TOPS-MODE approach

Singh, K.P.; Gupta, S.; Rai, P., 2013:
Predicting carcinogenicity of diverse chemicals using probabilistic neural network modeling approaches

Churpek, M.M.; Yuen, T.C.; Huber, M.T.; Park, S.Young.; Hall, J.B.; Edelson, D.P., 2012:
Predicting cardiac arrest on the wards: a nested case-control study

Abawajy, J.; Kelarev, A.; Chowdhury, M.; Stranieri, A.; Jelinek, H.F., 2014:
Predicting cardiac autonomic neuropathy category for diabetic data with missing values

Geerts, B.F.; Aarts, L.P.H.J.; Groeneveld, A.B.; Jansen, J.R.C., 2011:
Predicting cardiac output responses to passive leg raising by a PEEP-induced increase in central venous pressure, in cardiac surgery patients

Schröder, K.E.; Schwarzer, R.; Endler, N.S., 1997:
Predicting cardiac patients' quality of life from the characteristics of their spouses

Beckie, T.M.; Beckstead, J.W., 2010:
Predicting cardiac rehabilitation attendance in a gender-tailored randomized clinical trial

Verrier, R.L.; Kumar, K.; Wellenius, G.A., 2008:
Predicting cardiac resynchronization therapy response based on endothelial dysfunction: causal link or fellow traveler?

Calkins, S.D.; Graziano, P.A.; Berdan, L.E.; Keane, S.P.; Degnan, K.A., 2008:
Predicting cardiac vagal regulation in early childhood from maternal-child relationship quality during toddlerhood

Yang, H-Ling.; Lin, Y-Ping.; Long, Y.; Ma, Q-Ling.; Zhou, C., 2015:
Predicting cardioembolic stroke with the B-type natriuretic peptide test: a systematic review and meta-analysis

Savva, S.C.; Lamnisos, D.; Kafatos, A.G., 2013:
Predicting cardiometabolic risk: waist-to-height ratio or BMI. A meta-analysis

Parvatiyar, M.S.; Pinto, J.Renato.; Liang, J.; Potter, J.D., 2010:
Predicting cardiomyopathic phenotypes by altering Ca2+ affinity of cardiac troponin C

Chai, C-Ming.; Rasmussen, H.; Eriksen, M.; Hvoslef, A-Marie.; Evans, P.; Newton, B.B.; Videm, S., 2010:
Predicting cardiotoxicity propensity of the novel iodinated contrast medium GE-145: Ventricular fibrillation during left coronary arteriography in pigs

Liu, H.; Lee, S.S., 2011:
Predicting cardiovascular complications after liver transplantation: 007 to the rescue?

Jin, Z.; Sun, Y.; Cheng, A.C., 2010:
Predicting cardiovascular disease from real-time electrocardiographic monitoring: An adaptive machine learning approach on a cell phone

Junyent, M.; Martínez, M.; Borràs, Mè.; Coll, B.; Valdivielso, J.Manuel.; Vidal, T.; Sarró, F.; Roig, J.; Craver, L.; Fernández, E., 2010:
Predicting cardiovascular disease morbidity and mortality in chronic kidney disease in Spain. The rationale and design of NEFRONA: a prospective, multicenter, observational cohort study

Marshall, R.John., 2014:
Predicting cardiovascular events

Weintraub, W.S.; Diamond, G.A., 2008:
Predicting cardiovascular events with coronary calcium scoring

van Diepen, S.; Graham, M.M.; Nagendran, J.; Norris, C.M., 2015:
Predicting cardiovascular intensive care unit readmission after cardiac surgery: derivation and validation of the Alberta Provincial Project for Outcomes Assessment in Coronary Heart Disease (APPROACH) cardiovascular intensive care unit clinical prediction model from a registry cohort of 10,799 surgical cases

Sun, W.; Liu, D.; Gong, P.; Shi, X.; Wang, Y.; Wang, P.; Gong, W., 2015:
Predicting cardiovascular mortality in chronic kidney disease (CKD) patients

Kelly, A.S.; Steinberger, J.; Jacobs, D.R.; Hong, C-Ping.; Moran, A.; Sinaiko, A.R., 2011:
Predicting cardiovascular risk in young adulthood from the metabolic syndrome, its component risk factors, and a cluster score in childhood

Ricciardi, R.; Metter, E.Jeffery.; Cavanaugh, E.W.; Ghambaryan, A.; Talbot, L.A., 2009:
Predicting cardiovascular risk using measures of regional and total body fat

Lewis, L.Sam., 2010:
Predicting cardiovascular risk. How many are missed?

Parkes, G., 2010:
Predicting cardiovascular risk. Using risk scores with patients

Möller-Leimkühler, A.Maria.; Obermeier, M., 2008:
Predicting caregiver burden in first admission psychiatric patients. 2-year follow-up results

Meller, C.; Santamaria, R.M.; Connert, T.; Splieth, C., 2013:
Predicting caries by measuring its activity using quantitative light-induced fluorescence in vivo: a 2-year caries increment analysis

Wekesa, A.L.; Cross, K.S.; O'Donovan, O.; Dowdall, J.F.; O'Brien, O.; Doyle, M.; Byrne, L.; Phelan, J.P.; Ross, M.D.; Landers, R.; Harrison, M., 2015:
Predicting carotid artery disease and plaque instability from cell-derived microparticles

Narumi, S.; Sasaki, M.; Ohba, H.; Ogasawara, K.; Kobayashi, M.; Natori, T.; Hitomi, J.; Itagaki, H.; Takahashi, T.; Terayama, Y., 2014:
Predicting carotid plaque characteristics using quantitative color-coded T1-weighted MR plaque imaging: correlation with carotid endarterectomy specimens

Bosgra, S.; van de Steeg, E.; Vlaming, M.L.; Verhoeckx, K.C.; Huisman, M.T.; Verwei, M.; Wortelboer, H.M., 2015:
Predicting carrier-mediated hepatic disposition of rosuvastatin in man by scaling from individual transfected cell-lines in vitro using absolute transporter protein quantification and PBPK modeling

Frantzen, D.; San Miguel, C.; Kwak, D-Hoon., 2011:
Predicting case conviction and domestic violence recidivism: measuring the deterrent effects of conviction and protection order violations

Tiwari, V.; Furman, W.R.; Sandberg, W.S., 2014:
Predicting case volume from the accumulating elective operating room schedule facilitates staffing improvements

Li, D.; Jiang, Z.; Yu, W.; Du, L., 2011:
Predicting caspase substrate cleavage sites based on a hybrid SVM-PSSM method

Wang, W-Xu.; Yang, R.; Lai, Y-Cheng.; Kovanis, V.; Grebogi, C., 2011:
Predicting catastrophes in nonlinear dynamical systems by compressive sensing

Pothos, E.M.; Bailey, T.M., 2009:
Predicting category intuitiveness with the rational model, the simplicity model, and the generalized context model

Garibaldi, M.; Zarzoso, V.; Latcu, D.G.; Saoudi, N., 2013:
Predicting catheter ablation outcome in persistent atrial fibrillation using atrial dominant frequency and related spectral features

Chen, P-Jen.; Lin, M-Hsien.; Peng, L-Ning.; Liu, C-Liang.; Chang, C-Wei.; Lin, Y-Tsong.; Chen, L-Kung., 2012:
Predicting cause-specific mortality of older men living in the Veterans home by handgrip strength and walking speed: a 3-year, prospective cohort study in Taiwan

Head-Gordon, T.; Lynden-Bell, R.M.; Dowdle, J.R.; Rossky, P.J., 2012:
Predicting cavity formation free energy: how far is the Gaussian approximation valid?

Emmert-Streib, F.; Dehmer, M., 2010:
Predicting cell cycle regulated genes by causal interactions

Tyrrell, B.J.; Neilson, M.; Insall, R.H.; Machesky, L.M., 2014:
Predicting cell shapes in melanomas

Gerasimova, A.; Chavez, L.; Li, B.; Seumois, G.; Greenbaum, J.; Rao, A.; Vijayanand, P.; Peters, B., 2013:
Predicting cell types and genetic variations contributing to disease by combining GWAS and epigenetic data

Clarke, C.; Doolan, P.; Barron, N.; Meleady, P.; O'Sullivan, F.; Gammell, P.; Melville, M.; Leonard, M.; Clynes, M., 2011:
Predicting cell-specific productivity from CHO gene expression

Airoldi, E.M.; Huttenhower, C.; Gresham, D.; Lu, C.; Caudy, A.A.; Dunham, M.J.; Broach, J.R.; Botstein, D.; Troyanskaya, O.G., 2009:
Predicting cellular growth from gene expression signatures

Parviainen, A.; King, A.W.T.; Mutikainen, I.; Hummel, M.; Selg, C.; Hauru, L.K.J.; Sixta, H.; Kilpeläinen, I., 2014:
Predicting cellulose solvating capabilities of acid-base conjugate ionic liquids

Ferrari, R., 2011:
Predicting central sensitisation - whiplash patients

Yamada, M., 2012:
Predicting cerebral amyloid angiopathy-related intracerebral hemorrhages and other cerebrovascular disorders in Alzheimer's disease

Lambert, C.; Mannocci, L.; Lehodey, P.; Ridoux, V., 2015:
Predicting cetacean habitats from their energetic needs and the distribution of their prey in two contrasted tropical regions

Achmad, A.; Hanaoka, H.; Yoshioka, H.; Yamamoto, S.; Tominaga, H.; Araki, T.; Ohshima, Y.; Oriuchi, N.; Endo, K., 2012:
Predicting cetuximab accumulation in KRAS wild-type and KRAS mutant colorectal cancer using 64Cu-labeled cetuximab positron emission tomography

Verbeek, A.J.M.; Verbeek, J.F.M.; van Dijck, J.A.A.M.; Verbeek, Aé.L.M., 2015:
Predicting chance of disease: calculation using prediction rules

Conklin, H.M.; Li, C.; Xiong, X.; Ogg, R.J.; Merchant, T.E., 2008:
Predicting change in academic abilities after conformal radiation therapy for localized ependymoma

Mercer, S.H.; McMillen, J.Sturtz.; DeRosier, M.E., 2009:
Predicting change in children's aggression and victimization using classroom-level descriptive norms of aggression and pro-social behavior

Véronneau, M-Hélène.; Dishion, T.J., 2011:
Predicting change in early adolescent problem behavior in the middle school years: a mesosystemic perspective on parenting and peer experiences

Phan, H.P., 2007:
Predicting change in epistemological beliefs, reflective thinking and learning styles: a longitudinal study

Sands, L.P.; Xu, H.; Craig, B.A.; Eng, C.; Covinsky, K.E., 2008:
Predicting change in functional status over quarterly intervals for older adults enrolled in the PACE community-based long-term care program

LeMoult, J.; Carver, C.S.; Johnson, S.L.; Joormann, J., 2015:
Predicting change in symptoms of depression during the transition to university: the roles of BDNF and working memory capacity

Lee, J.Hyuk.; Kim, D.Hoon.; Kim, K.; Rhee, J.Eui.; Kim, T.Youn.; Jo, Y.Hwan.; Lee, J.Hee.; Suh, G.Joon.; Hwang, S.Sik.; Lee, C.C.; Singer, A.J., 2010:
Predicting change of hemoglobin after transfusion in hemodynamically stable anemic patients in emergency department

Mayeux, L.; Bellmore, A.D.; Cillessen, A.H.N., 2008:
Predicting changes in adjustment using repeated measures of sociometric status

Amorose, A.J.; Anderson-Butcher, D.; Cooper, J., 2009:
Predicting changes in athletes' well being from changes in need satisfaction over the course of a competitive season

Morton, M.J.; Armstrong, D.; Abi Gerges, N.; Bridgland-Taylor, M.; Pollard, C.E.; Bowes, J.; Valentin, J-P., 2014:
Predicting changes in cardiac myocyte contractility during early drug discovery with in vitro assays

Leung, T.F.; Ko, F.W.S.; Wong, G.W.K.; Li, C.Y.; Yung, E.; Hui, D.S.C.; Lai, C.K.W., 2009:
Predicting changes in clinical status of young asthmatics: clinical scores or objective parameters?

Batlla, D.; Benech-Arnold, R.Luis., 2010:
Predicting changes in dormancy level in natural seed soil banks

Jackson, T.; Chen, H., 2008:
Predicting changes in eating disorder symptoms among adolescents in China: an 18-month prospective study

Buchheit, M.; Rabbani, A.; Beigi, H.Taghi., 2014:
Predicting changes in high-intensity intermittent running performance with acute responses to short jump rope workouts in children

Sun, J.; McNaughton, C.D.; Zhang, P.; Perer, A.; Gkoulalas-Divanis, A.; Denny, J.C.; Kirby, J.; Lasko, T.; Saip, A.; Malin, B.A., 2014:
Predicting changes in hypertension control using electronic health records from a chronic disease management program

Erhart, J.C.; Mündermann, A.; Mündermann, L.; Andriacchi, T.P., 2008:
Predicting changes in knee adduction moment due to load-altering interventions from pressure distribution at the foot in healthy subjects

Peyre, H.; Bernard, J.Y.; Forhan, A.; Charles, M-Aline.; D.A.ostini, M.; Heude, B.; Ramus, F., 2014:
Predicting changes in language skills between 2 and 3 years in the EDEN mother-child cohort

Sorkin, D.H.; Rook, K.S.; Heckhausen, J.; Billimek, J., 2010:
Predicting changes in older adults' interpersonal control strivings

Araújo-Soares, V.; McIntyre, T.; Sniehotta, F.F., 2008:
Predicting changes in physical activity among adolescents: the role of self-efficacy, intention, action planning and coping planning

McDonough, M.H.; Sabiston, C.M.; Wrosch, C., 2014:
Predicting changes in posttraumatic growth and subjective well-being among breast cancer survivors: the role of social support and stress

Janssen, D.J.A.; Spruit, M.A.; Schols, J.M.G.A.; Cox, B.; Nawrot, T.S.; Curtis, J.Randall.; Wouters, E.F.M., 2012:
Predicting changes in preferences for life-sustaining treatment among patients with advanced chronic organ failure

Tian, J.; Wu, N.; Chu, X.; Fan, Y., 2010:
Predicting changes in protein thermostability brought about by single- or multi-site mutations

Åkerstedt, Törn.; Nordin, M.; Alfredsson, L.; Westerholm, P.; Kecklund, Göran., 2012:
Predicting changes in sleep complaints from baseline values and changes in work demands, work control, and work preoccupation--the WOLF-project

Graber, J.E.; Huang, E.S.; Drum, M.L.; Chin, M.H.; Walters, A.E.; Heuer, L.; Tang, H.; Schaefer, C.T.; Quinn, M.T., 2008:
Predicting changes in staff morale and burnout at community health centers participating in the health disparities collaboratives

Chan, S.; Chiu, H.; Chien, W-tong.; Goggins, W.; Thompson, D.; Lam, L.; Hong, B., 2009:
Predicting changes in the health-related quality of life of Chinese depressed older people

Guo, J.; Hall, K.D., 2011:
Predicting changes of body weight, body fat, energy expenditure and metabolic fuel selection in C57BL/6 mice

Smith, D.L.; Hay, S.I.; Noor, A.M.; Snow, R.W., 2010:
Predicting changing malaria risk after expanded insecticide-treated net coverage in Africa

Wang, X.; Li, C.; Huang, T.; Duan, S., 2013:
Predicting chaos in memristive oscillator via harmonic balance method

Gomadam, P.M.; Brown, J.R.; Scott, E.R.; Schmidt, C.L., 2010:
Predicting charge-times of implantable cardioverter defibrillators

Clausznitzer, D.; Micali, G.; Neumann, S.; Sourjik, V.; Endres, R.G., 2015:
Predicting chemical environments of bacteria from receptor signaling

Solimeo, R.; Zhang, J.; Kim, M.; Sedykh, A.; Zhu, H., 2013:
Predicting chemical ocular toxicity using a combinatorial QSAR approach

Chen, L.; Lu, J.; Zhang, J.; Feng, K-Rui.; Zheng, M-Yue.; Cai, Y-Dong., 2013:
Predicting chemical toxicity effects based on chemical-chemical interactions

Huff, J., 2011:
Predicting chemicals causing cancer in animals as human carcinogens

Chen, Y.; Yang, Y.; Yuan, Z.; Wang, C.; Shi, Y., 2012:
Predicting chemosensitivity in osteosarcoma prior to chemotherapy: An investigational study of biomarkers with immunohistochemistry

Lagro, J.; Studenski, S.A.; Olde Rikkert, M.G.M., 2012:
Predicting chemotherapy toxicity in older adults and the importance of geriatric assessment

Hurria, A.; Togawa, K.; Mohile, S.G.; Owusu, C.; Klepin, H.D.; Gross, C.P.; Lichtman, S.M.; Gajra, A.; Bhatia, S.; Katheria, V.; Klapper, S.; Hansen, K.; Ramani, R.; Lachs, M.; Wong, F.Lennie.; Tew, W.P., 2011:
Predicting chemotherapy toxicity in older adults with cancer: a prospective multicenter study

Woody, N.M.; Videtic, G.M.M.; Stephans, K.L.; Djemil, T.; Kim, Y.; Xia, P., 2012:
Predicting chest wall pain from lung stereotactic body radiotherapy for different fractionation schemes

Taylor, J.; Baldwin, N.; Spencer, N., 2008:
Predicting child abuse and neglect: ethical, theoretical and methodological challenges

Begle, A.Moreland.; Dumas, J.E.; Hanson, R.F., 2010:
Predicting child abuse potential: an empirical investigation of two theoretical frameworks

Sledjeski, E.M.; Dierker, L.C.; Bird, H.R.; Canino, G., 2009:
Predicting child maltreatment among Puerto Rican children from migrant and non-migrant families

Langer, S.L.; Crain, A.Lauren.; Senso, M.M.; Levy, R.L.; Sherwood, N.E., 2016:
Predicting child physical activity and screen time: parental support for physical activity and general parenting styles

Sharma, M.; Wagner, D.I.; Wilkerson, J., 2007:
Predicting childhood obesity prevention behaviors using social cognitive theory

Elmore, S.; Sharma, M., 2014:
Predicting childhood obesity prevention behaviors using social cognitive theory among upper elementary African-American children

Koyama, T.; Inada, N.; Tsujii, H.; Kurita, H., 2008:
Predicting children with pervasive developmental disorders using the Wechsler Intelligence Scale for Children-Third Edition

Mesquita, A.R.; Soares, I.; Roisman, G.I.; van IJzendoorn, M.; Bakermans-Kranenburg, M.; Luijk, M.; Tiemeier, H.; Belsky, J., 2015:
Predicting children's attachment behaviors from the interaction between oxytocin and glucocorticoid receptors polymorphisms

Lee, S-Jung.; Bartolic, S.; Vandewater, E.A., 2010:
Predicting children's media use in the USA: differences in cross-sectional and longitudinal analysis

Olsson, K.A.; Kenardy, J.A.; D.Y.ung, A.C.; Spence, S.H., 2008:
Predicting children's post-traumatic stress symptoms following hospitalization for accidental injury: combining the Child Trauma Screening Questionnaire and heart rate

Taylor, C.L.; Zubrick, S.R., 2009:
Predicting children's speech, language and reading impairment over time

Spencer, K., 2010:
Predicting children's word-reading accuracy for common English words: the effect of word transparency and complexity

Spencer, K., 2007:
Predicting children's word-spelling difficulty for common English words from measures of orthographic transparency, phonemic and graphemic length and word frequency

Hur, K.; Hennig, R.G.; Escobedo, F.A.; Wiesner, U., 2012:
Predicting chiral nanostructures, lattices and superlattices in complex multicomponent nanoparticle self-assembly

Yanosky, J.D.; Paciorek, C.J.; Suh, H.H., 2009:
Predicting chronic fine and coarse particulate exposures using spatiotemporal models for the Northeastern and Midwestern United States

Ebeling, P.R., 2014:
Predicting chronic graft-versus-host disease after hematopoietic stem cell transplantation

DeVille, J.; Smith, D.H.; Johnson, E.S.; Yang, X.; Petrik, A.F.; Weiss, J.R.; Thorp, M.L., 2012:
Predicting chronic kidney disease outcomes: are two estimated glomerular filtration rates better than one?

Ezekowitz, J.A.; Armstrong, P.W.; Granger, C.B.; Theroux, P.; Stebbins, A.; Kim, R.J.; Patel, M.R., 2010:
Predicting chronic left ventricular dysfunction 90 days after ST-segment elevation myocardial infarction: An Assessment of Pexelizumab in Acute Myocardial Infarction (APEX-AMI) Substudy

Gerke, A.K.; Tang, F.; Yang, M.; Foster, E.D.; Cavanaugh, J.E.; Polgreen, P.M., 2014:
Predicting chronic obstructive pulmonary disease hospitalizations based on concurrent influenza activity

Dickinson, K.J.; Thomas, M.; Fawole, A.S.; Lyndon, P.J.; White, C.M., 2008:
Predicting chronic post-operative pain following laparoscopic inguinal hernia repair

Nonclercq, O.; Berquin, A., 2012:
Predicting chronicity in acute back pain: validation of a French translation of the Örebro Musculoskeletal Pain Screening Questionnaire

Curto, T.M.; Lagier, R.J.; Lok, A.S.; Everhart, J.E.; Rowland, C.M.; Sninsky, J.J.; Szabo, G.; Banner, B.F.; Cormier, M.; Giansiracusa, D.; Bonkovsky, H.L.; Borders, G.; Kelley, M.; Di Bisceglie, A.M.; Bacon, B.; Neuschwander-Tetri, B.; Brunt, E.M.; King, D.; Dienstag, J.L.; Chung, R.T.; Reid, A.E.; Bhan, A.K.; Molchen, W.A.; Lundmark, D.P.; Everson, G.T.; Trouillot, T.; Kugelmas, M.; Nash, S.Russell.; DeSanto, J.; McKinley, C.; Morgan, T.R.; Hoefs, J.C.; Craig, J.R.; Jamal, M.Mazen.; Sheikh, M., 2012:
Predicting cirrhosis and clinical outcomes in patients with advanced chronic hepatitis C with a panel of genetic markers (CRS7)

Zhang, Y.; Xiong, Q.; Yang, G.; Li, M.; Zhang, J., 2007:
Predicting cis/trans structure of alkene based on infrared spectra

Gaspari, F.; Cravedi, P.; Mandalà, M.; Perico, N.; de Leon, F.Rodríguez.; Stucchi, N.; Ferrari, S.; Labianca, R.; Remuzzi, G.; Ruggenenti, P., 2011:
Predicting cisplatin-induced acute kidney injury by urinary neutrophil gelatinase-associated lipocalin excretion: a pilot prospective case-control study

Ibáñez, A.; Larrañaga, P.; Bielza, C., 2010:
Predicting citation count of Bioinformatics papers within four years of publication

Steyerberg, E.W.; Lingsma, H.F., 2008:
Predicting citations: Validating prediction models

Obach, R.Scott., 2011:
Predicting clearance in humans from in vitro data

Huckabee, C.M.; Huskins, W.Charles.; Murray, P.R., 2009:
Predicting clearance of colonization with vancomycin-resistant Enterococci and methicillin-resistant Staphylococcus aureus by use of weekly surveillance cultures

Mao, Y.; Lu, Z., 2014:
Predicting clicks of PubMed articles

Zhong, B.; Xu, Y.Jun., 2014:
Predicting climate change effects on surface soil organic carbon of Louisiana, USA

Archetti, M.; Richardson, A.D.; O'Keefe, J.; Delpierre, N., 2013:
Predicting climate change impacts on the amount and duration of autumn colors in a New England forest

Kerr, R.A., 2011:
Predicting climate change. Vital details of global warming are eluding forecasters

Ghany, M.G.; Lok, A.S.F.; Everhart, J.E.; Everson, G.T.; Lee, W.M.; Curto, T.M.; Wright, E.C.; Stoddard, A.M.; Sterling, R.K.; Di Bisceglie, A.M.; Bonkovsky, H.L.; Morishima, C.; Morgan, T.R.; Dienstag, J.L., 2010:
Predicting clinical and histologic outcomes based on standard laboratory tests in advanced chronic hepatitis C

Wataha, J.C., 2012:
Predicting clinical biological responses to dental materials

Zhao, D.; Zhang, W.; Li, X-guang.; Wang, X-bing.; Zhang, L-feng.; Li, M.; Li, Y-fen.; Tian, H-mei.; Song, P-pei.; Liu, J.; Chang, Q-yun.; Wu, L-ying., 2012:
Predicting clinical chemo-sensitivity of primary ovarian cancer using adenosine triphosphate-tumor chemosensitivity assay combined with detection of drug resistance genes

Trinidad, K.J.; Schmidt, J.D.; Register-Mihalik, J.K.; Groff, D.; Goto, S.; Guskiewicz, K.M., 2014:
Predicting clinical concussion measures at baseline based on motivation and academic profile

Burns, J.A.; Kobler, J.B.; Heaton, J.T.; Anderson, R.Rox.; Zeitels, S.M., 2008:
Predicting clinical efficacy of photoangiolytic and cutting/ablating lasers using the chick chorioallantoic membrane model: implications for endoscopic voice surgery

Kay, N.E., 2012:
Predicting clinical outcome in B-chronic lymphocytic leukemia

Shanafelt, T.D., 2010:
Predicting clinical outcome in CLL: how and why

Wu, O.; Batista, L.M.; Lima, F.O.; Vangel, M.G.; Furie, K.L.; Greer, D.M., 2011:
Predicting clinical outcome in comatose cardiac arrest patients using early noncontrast computed tomography

Khan, M.Ali.; Khawaja, M.Naveed.; Hakeem, F., 2012:
Predicting clinical outcome in diabetics vs. non diabetics with acute myocardial infarction after thrombolysis, using ECG as a tool

Yoshikawa, H.; Maranon, D.G.; Battaglia, C.L.R.; Ehrhart, E.J.; Charles, J.B.; Bailey, S.M.; LaRue, S.M., 2014:
Predicting clinical outcome in feline oral squamous cell carcinoma: tumour initiating cells, telomeres and telomerase

Ramus, S.J.; Elmasry, K.; Luo, Z.; Gammerman, A.; Lu, K.; Ayhan, A.; Singh, N.; McCluggage, W.Glenn.; Jacobs, I.J.; Whittaker, J.C.; Gayther, S.A., 2008:
Predicting clinical outcome in patients diagnosed with synchronous ovarian and endometrial cancer

Iacopetta, B.; Kawakami, K.; Watanabe, T., 2009:
Predicting clinical outcome of 5-fluorouracil-based chemotherapy for colon cancer patients: is the CpG island methylator phenotype the 5-fluorouracil-responsive subgroup?

John, T.; Black, M.A.; Toro, T.T.; Leader, D.; Gedye, C.A.; Davis, I.D.; Guilford, P.J.; Cebon, J.S., 2008:
Predicting clinical outcome through molecular profiling in stage III melanoma

Garrett, A.S.; Lock, J.; Datta, N.; Beenhaker, J.; Kesler, S.R.; Reiss, A.L., 2015:
Predicting clinical outcome using brain activation associated with set-shifting and central coherence skills in Anorexia Nervosa

Saposnik, G.; Reeves, M.J.; Johnston, S.Claiborne.; Bath, P.M.W.; Ovbiagele, B., 2013:
Predicting clinical outcomes after thrombolysis using the iScore: results from the Virtual International Stroke Trials Archive

Nikneshan, D.; Raptis, R.; Pongmoragot, J.; Zhou, L.; Johnston, S.Claiborne.; Saposnik, G., 2014:
Predicting clinical outcomes and response to thrombolysis in acute stroke patients with diabetes

Ghany, M.G.; Kim, H-Young.; Stoddard, A.; Wright, E.C.; Seeff, L.B.; Lok, A.S.F.; Szabo, G.; Banner, B.F.; Cormier, M.; Giansiracusa, D.; Bonkovsky, H.L.; Borders, G.; Kelley, M.; Di Bisceglie, A.M.; Bacon, B.; Neuschwander-Tetri, B.; Brunt, E.M.; King, D.; Dienstag, J.L.; Chung, R.T.; Reid, A.E.; Bhan, A.K.; Molchen, W.A.; Lundmark, D.P.; Everson, G.T.; Trouillot, T.; Kugelmas, M.; Nash, S.Russell.; DeSanto, J.; McKinley, C.; Morgan, T.R.; Hoefs, J.C.; Craig, J.R.; Jamal, M.Mazen.; Sheikh, M.;, 2012:
Predicting clinical outcomes using baseline and follow-up laboratory data from the hepatitis C long-term treatment against cirrhosis trial

Singh, S.; Kim, W.Ray.; Talwalkar, J.A., 2013:
Predicting clinical outcomes with elastography in primary biliary cirrhosis: one step closer?

Lu, Y.; Burykin, A.; Deem, M.W.; Buchman, T.G., 2009:
Predicting clinical physiology: a Markov chain model of heart rate recovery after spontaneous breathing trials in mechanically ventilated patients

Bakshi, R.; Neema, M.; Healy, B.C.; Liptak, Z.; Betensky, R.A.; Buckle, G.J.; Gauthier, S.A.; Stankiewicz, J.; Meier, D.; Egorova, S.; Arora, A.; Guss, Z.D.; Glanz, B.; Khoury, S.J.; Guttmann, C.R.G.; Weiner, H.L., 2008:
Predicting clinical progression in multiple sclerosis with the magnetic resonance disease severity scale

Stonnington, C.M.; Chu, C.; Klöppel, S.; Jack, C.R.; Ashburner, J.; Frackowiak, R.S.J., 2010:
Predicting clinical scores from magnetic resonance scans in Alzheimer's disease

Broglio, K.R.; Stivers, D.N.; Berry, D.A., 2014:
Predicting clinical trial results based on announcements of interim analyses

Schwiegerling, J., 2010:
Predicting clinical visual acuity of presbyopia treatments

Gonda, T.; Deane, F.P.; Murugesan, G., 2012 :
Predicting clinically significant change in an inpatient program for people with severe mental illness

Hsieh, Y-wei.; Lin, K-chung.; Wu, C-yi.; Lien, H-yu.; Chen, J-lon.; Chen, C-chi.; Chang, W-han., 2014:
Predicting clinically significant changes in motor and functional outcomes after robot-assisted stroke rehabilitation

Chang, S.Min.; Hakeem, A.; Nagueh, S.F., 2009:
Predicting clinically unrecognized coronary artery disease: use of two- dimensional echocardiography

Sieburg, H.B.; Rezner, B.D.; Muller-Sieburg, C.E., 2011:
Predicting clonal self-renewal and extinction of hematopoietic stem cells

Yu, Q.; Long, C.; Lv, Y.; Shao, H.; He, P.; Duan, Z., 2015:
Predicting co-author relationship in medical co-authorship networks

Zeliger, H.I.; Pan, Y.; Rea, W.J., 2013:
Predicting co-morbidities in chemically sensitive individuals from exhaled breath analysis

Tzilos, G.K.; Rhodes, G.L.; Ledgerwood, D.M.; Greenwald, M.K., 2009:
Predicting cocaine group treatment outcome in cocaine-abusing methadone patients

Giraud, A-Lise.; Lee, H-Jeong., 2007:
Predicting cochlear implant outcome from brain organisation in the deaf

Luciano, M.; Mõttus, R.; Harris, S.E.; Davies, G.; Payton, A.; Ollier, W.E.R.; Horan, M.A.; Starr, J.M.; Porteous, D.J.; Pendleton, N.; Deary, I.J., 2015:
Predicting cognitive ability in ageing cohorts using Type 2 diabetes genetic risk

Duff, K.; Schoenberg, M.R.; Patton, D.E.; Mold, J.W.; Scott, J.G.; Adams, R.L., 2007:
Predicting cognitive change across 3 years in community-dwelling elders

Duff, K.; Beglinger, L.J.; Moser, D.J.; Paulsen, J.S.; Schultz, S.K.; Arndt, S., 2010:
Predicting cognitive change in older adults: the relative contribution of practice effects

Duff, K.; Beglinger, L.J.; Moser, D.J.; Paulsen, J.S., 2010:
Predicting cognitive change within domains

Kandel, B.M.; Wolk, D.A.; Gee, J.C.; Avants, B., 2014:
Predicting cognitive data from medical images using sparse linear regression

Gavett, B.E.; Ozonoff, A.; Doktor, V.; Palmisano, J.; Nair, A.K.; Green, R.C.; Jefferson, A.L.; Stern, R.A., 2010:
Predicting cognitive decline and conversion to Alzheimer's disease in older adults using the NAB List Learning test

Lopez, O.L.; Schwam, E.; Cummings, J.; Gauthier, S.; Jones, R.; Wilkinson, D.; Waldemar, G.; Zhang, R.; Schindler, R., 2011:
Predicting cognitive decline in Alzheimer's disease: an integrated analysis

D.M.rchi, F.; Carecchio, M.; Cantello, R.; Comi, C., 2014:
Predicting cognitive decline in Parkinson's disease: can we ask the genes?

Shaffer, J.L.; Petrella, J.R.; Sheldon, F.C.; Choudhury, K.Roy.; Calhoun, V.D.; Coleman, R.Edward.; Doraiswamy, P.Murali., 2013:
Predicting cognitive decline in subjects at risk for Alzheimer disease by using combined cerebrospinal fluid, MR imaging, and PET biomarkers

Kaffashian, S.; Dugravot, A.; Elbaz, A.; Shipley, M.J.; Sabia, Séverine.; Kivimäki, M.; Singh-Manoux, A., 2013:
Predicting cognitive decline: a dementia risk score vs. the Framingham vascular risk scores

Jaillard, A.; Grand, S.; L.B.s, J.François.; Hommel, M., 2010:
Predicting cognitive dysfunctioning in nondemented patients early after stroke

Henderson, J.M.; Shinkareva, S.V.; Wang, J.; Luke, S.G.; Olejarczyk, J., 2014:
Predicting cognitive state from eye movements

Shikanov, S.; Lifshitz, D.A.; Deklaj, T.; Katz, M.H.; Shalhav, A.L., 2010:
Predicting collecting system transection at laparoscopic partial nephrectomy: analysis of tumor parameters

Wright, P.; Alex, A.; Pullen, F., 2014:
Predicting collision-induced dissociation spectra: semi-empirical calculations as a rapid and effective tool in software-aided mass spectral interpretation

Zhang, Y.; Su, Y.Ying.; Ye, H.; Xiao, S.Ying.; Chen, W.Bi.; Zhao, J.Wei., 2011:
Predicting comatose patients with acute stroke outcome using middle-latency somatosensory evoked potentials

Mounce, S.R.; Shepherd, W.; Sailor, G.; Shucksmith, J.; Saul, A.J., 2014:
Predicting combined sewer overflows chamber depth using artificial neural networks with rainfall radar data

Margolin, S., 2012:
Predicting comfort of flame resistant clothing

Young, B.J.; Furman, W., 2014:
Predicting commitment in young adults' physically aggressive and sexually coercive dating relationships

Johnson, N.C.; Angelard, C.; Sanders, I.R.; Kiers, E.Toby., 2014:
Predicting community and ecosystem outcomes of mycorrhizal responses to global change

Petrie, D.A.; Campbell, S.G.; Ross, J.A., 2015:
Predicting community emergency medicine needs and emergency physician compensation modeling; two sides of the same coin?

Crowson, H.Michael.; Brandes, J.A., 2010:
Predicting community opposition to inclusion in schools: the role of social dominance, contact, intergroup anxiety, and economic conservatism

Doyle, M.; Carter, S.; Shaw, J.; Dolan, M., 2012:
Predicting community violence from patients discharged from acute mental health units in England

Postma, D.F.; van Werkhoven, C.H.; Oosterheert, J.Jelrik.; Bonten, M.J.M., 2014:
Predicting community-acquired pneumonia etiology

Troia, M.J.; Gido, K.B., 2014:
Predicting community-environment relationships of stream fishes across multiple drainage basins: insights into model generality and the effect of spatial extent

Wheeler, A.J.; Gundy, J.M.; DeBerard, M.Scott., 2012:
Predicting compensation and medical costs of lumbar fusion patients receiving workers' compensation in utah using presurgical biopsychosocial variables

Aksenov, A.A.; Kapron, J.; Davis, C.E., 2012:
Predicting compensation voltage for singly-charged ions in high-field asymmetric waveform ion mobility spectrometry (FAIMS)

Hasford, J.; Baccarani, M.; Hoffmann, V.; Guilhot, J.; Saussele, S.; Rosti, G.; Guilhot, Fçois.; Porkka, K.; Ossenkoppele, G.; Lindoerfer, D.; Simonsson, B.; Pfirrmann, M.; Hehlmann, R., 2011:
Predicting complete cytogenetic response and subsequent progression-free survival in 2060 patients with CML on imatinib treatment: the EUTOS score

Park, M.J.; Yamazaki, Y.; Yonekura, Y.; Yukawa, K.; Ishikawa, H.; Kiuchi, T.; Green, J., 2012:
Predicting complete loss to follow-up after a health-education program: number of absences and face-to-face contact with a researcher

Dlugos, D.J., 2012:
Predicting complete remission in pediatric epilepsy: a challenge to do better

Perez, R.O.; Habr-Gama, A.; São Julião, G.P.; Lynn, P.B.; Sabbagh, C.; Proscurshim, I.; Campos, F.G.; Gama-Rodrigues, J.; Nahas, S.C.; Buchpiguel, C.A., 2015:
Predicting complete response to neoadjuvant CRT for distal rectal cancer using sequential PET/CT imaging

Ubbink, D.T.; Lindeboom, R.; Eskes, A.M.; Brull, H.; Legemate, D.A.; Vermeulen, H., 2016:
Predicting complex acute wound healing in patients from a wound expertise centre registry: a prognostic study

Dikicioglu, D.; Pir, Pınar.; Oliver, S.G., 2014:
Predicting complex phenotype-genotype interactions to enable yeast engineering: Saccharomyces cerevisiae as a model organism and a cell factory

Prakasvudhisarn, C.; Wolschann, P.; Lawtrakul, L., 2009:
Predicting complexation thermodynamic parameters of β-cyclodextrin with chiral guests by using swarm intelligence and support vector machines

Bucci, S.; Birchwood, M.; Twist, L.; Tarrier, N.; Emsley, R.; Haddock, G., 2013:
Predicting compliance with command hallucinations: anger, impulsivity and appraisals of voices' power and intent

Wolfgang, C.Lee., 2014:
Predicting complicated choledocholithiasis

Cawley, J.; Sweeney, M.J.; Kurian, M.; Beane, S., 2008:
Predicting complications after bariatric surgery using obesity-related co-morbidities

McCarthy, C.M.; Mehrara, B.J.; Riedel, E.; Davidge, K.; Hinson, A.; Disa, J.J.; Cordeiro, P.G.; Pusic, A.L., 2008:
Predicting complications following expander/implant breast reconstruction: an outcomes analysis based on preoperative clinical risk

Akkermans, J.; Payne, B.; von Dadelszen, P.; Groen, H.; Vries, J.de.; Magee, L.A.; Mol, B.Willem.; Ganzevoort, W., 2015 :
Predicting complications in pre-eclampsia: external validation of the fullPIERS model using the PETRA trial dataset

Lee, G.; Gurm, H.S.; Syed, Z., 2013:
Predicting complications of percutaneous coronary intervention using a novel support vector method

Elsayed, H., 2009:
Predicting complications of top hat prosthesis in aortic valve replacements: suspicion can save lives

Stadnicka, J.; Schirmer, K.; Ashauer, R., 2012:
Predicting concentrations of organic chemicals in fish by using toxicokinetic models

Wei, T.; Liang, X.; He, Y.; Zang, Y.; Han, Z.; Caramazza, A.; Bi, Y., 2012:
Predicting conceptual processing capacity from spontaneous neuronal activity of the left middle temporal gyrus

Miller, C.; Kapp, S.; Newall, N.; Lewin, G.; Carville, K.; Santamaria, N.; Karimi, L., 2011:
Predicting concordance with multilayer compression bandaging

Scott-Sheldon, L.A.J.; Carey, M.P.; Vanable, P.A.; Senn, T.E.; Coury-Doniger, P.; Urban, M.A., 2011:
Predicting condom use among STD clinic patients using the Information - Motivation-Behavioral Skills (IMB) model

Villarruel, A.M.; Jemmott, J.B.; Jemmott, L.S.; Ronis, D.L., 2007:
Predicting condom use among sexually experienced Latino adolescents

Malcolm, S.; Huang, S.; Cordova, D.; Freitas, D.; Arzon, M.; Jimenez, G.Leon.; Pantin, H.; Prado, G., 2014:
Predicting condom use attitudes, norms, and control beliefs in Hispanic problem behavior youth: the effects of family functioning and parent-adolescent communication about sex on condom use

Eggers, S.M.; Aarø, L.E.; Bos, A.E.R.; Mathews, C.; de Vries, H., 2015 :
Predicting condom use in South Africa: a test of two integrative models

Dhulesia, A.; Bodenhausen, G.; Abergel, D., 2008:
Predicting conformational entropy of bond vectors in proteins by networks of coupled rotators

Naro-Maciel, E.; Gaughran, S.J.; Putman, N.F.; Amato, G.; Arengo, F.; Dutton, P.H.; McFadden, K.W.; Vintinner, E.C.; Sterling, E.J., 2014:
Predicting connectivity of green turtles at Palmyra Atoll, central Pacific: a focus on mtDNA and dispersal modelling

Spirollari, J.; Wang, J.T.L.; Zhang, K.; Bellofatto, V.; Park, Y.; Shapiro, B.A., 2010:
Predicting consensus structures for RNA alignments via pseudo-energy minimization

Duffield, M.; Cooper, I.; McAlister, E.; Bayliss, M.; Ford, D.; Oyston, P., 2011:
Predicting conserved essential genes in bacteria: in silico identification of putative drug targets

Horan, K.; Shelton, C.R.; Girke, T., 2010:
Predicting conserved protein motifs with Sub-HMMs

Goel, S.; Hofman, J.M.; Lahaie, Sébastien.; Pennock, D.M.; Watts, D.J., 2010:
Predicting consumer behavior with Web search

Calvert, G.A.; Brammer, M.J., 2012:
Predicting consumer behavior: using novel mind-reading approaches

Olsen, N.Veflen.; Sijtsema, S.J.; Hall, G., 2011:
Predicting consumers' intention to consume ready-to-eat meals. The role of moral attitude

Chen, P.; Huang, D-Shuang.; Zhao, X-Ming.; Li, X., 2008:
Predicting contact map using radial basis function neural network with conformational energy function

Forquer, H.A.; Christensen, J.L.; Tan, A.S.L., 2015:
Predicting continuance-findings from a longitudinal study of older adults using an eHealth newsletter

Faraggi, E.; Yang, Y.; Zhang, S.; Zhou, Y., 2010:
Predicting continuous local structure and the effect of its substitution for secondary structure in fragment-free protein structure prediction

Chuang, M-Lung.; Lin, I-Feng.; Vintch, J.R.E.; Liao, Y-Fang., 2008:
Predicting continuous positive airway pressure from a modified split-night protocol in moderate to severe obstructive sleep apnea-hypopnea syndrome

León, F.R., 2014:
Predicting contraceptive use from an egalitarian model of women's overall household power vis-à-vis conventional power models and third variables

Carlson, K., 2014:
Predicting contrast in sentences with and without focus marking

Yuh, W.T.C.; Mayr, N.A.; Jarjoura, D.; Wu, D.; Grecula, J.C.; Lo, S.S.; Edwards, S.M.; Magnotta, V.A.; Sammet, S.; Zhang, H.; Montebello, J.F.; Fowler, J.; Knopp, M.V.; Wang, J.Z., 2009:
Predicting control of primary tumor and survival by DCE MRI during early therapy in cervical cancer

Chapman, R.M.; Mapstone, M.; McCrary, J.W.; Gardner, M.N.; Porsteinsson, A.; Sandoval, T.C.; Guillily, M.D.; Degrush, E.; Reilly, L.A., 2011:
Predicting conversion from mild cognitive impairment to Alzheimer's disease using neuropsychological tests and multivariate methods

Morrow, S.A.; Fraser, J.Alexander.; Nicolle, D.; Kremenchutzky, M., 2010:
Predicting conversion to MS--the role of a history suggestive of demyelination

Balota, D.A.; Tse, C-Shing.; Hutchison, K.A.; Spieler, D.H.; Duchek, J.M.; Morris, J.C., 2010:
Predicting conversion to dementia of the Alzheimer's type in a healthy control sample: the power of errors in Stroop color naming

Elias, M.F.; Davey, A., 2009:
Predicting conversion to mild cognitive impairment: some error is the price of much truth

Qiu, H.; Vijver, M.G.; He, E.; Peijnenburg, W.J.G.M., 2014:
Predicting copper toxicity to different earthworm species using a multicomponent Freundlich model

Golbuu, Y.; Wolanski, E.; Idechong, J.Wasai.; Victor, S.; Isechal, A.Lukes.; Oldiais, N.Wenty.; Idip, D.; Richmond, R.H.; van Woesik, R., 2013:
Predicting coral recruitment in Palau's complex reef archipelago

Richards, Z.T.; Hobbs, J-Paul.A., 2014:
Predicting coral species richness: the effect of input variables, diversity and scale

Li, Q.; Zang, J.; Liu, D.; Piao, X.; Lai, C.; Li, D., 2014:
Predicting corn digestible and metabolizable energy content from its chemical composition in growing pigs

Brieger, D.; Elsik, M.; Gore, J.M.; Knobel, E.; Nussbacher, A.; Piegas, L.S.; Allegrone, J.; Anderson, F.A.; Avezum, A., 2007:
Predicting coronary artery bypass graft surgery in acute coronary syndromes

Colak, M.Cengiz.; Colak, C.; Kocatürk, H.; Sağiroğlu, S.; Barutçu, I., 2008:
Predicting coronary artery disease using different artificial neural network models

Chen, Q.; Li, G.; Leong, T-Yun.; Heng, C-Kiat., 2007:
Predicting coronary artery disease with medical profile and gene polymorphisms data

Anonymous, 1966:
Predicting coronary disease

Israni, A.K.; Snyder, J.J.; Skeans, M.A.; Peng, Y.; Maclean, J.R.; Weinhandl, E.D.; Kasiske, B.L.; Brennan, D.; Connaire, J.; Israni, A.; Gaston, R.; Gill, J.; Legendre, C.; Kreis, H.; Lopau, K.; Matas, A.; Kasiske, B.; Pesavento, T.; Pilmore, H.; Pirsch, J.; Tanabe, K.; Setoguchi, K.; Torres, A.; Hernandez, D.; Porrini, E.; Vanrenterghem, Y.; Watschinger, B., 2010:
Predicting coronary heart disease after kidney transplantation: Patient Outcomes in Renal Transplantation (PORT) Study

McSweeney, J.; Cleves, M.A.; Fischer, E.P.; Moser, D.K.; Wei, J.; Pettey, C.; Rojo, M.O.; Armbya, N., 2015:
Predicting coronary heart disease events in women: a longitudinal cohort study

Dwivedi, J.; Sutcliffe, S.; Easterbrook, L.; Woods, C.; Maguire, G.P., 2015:
Predicting coronary heart disease in remote settings: a prospective, cross-sectional observational study

Vrentzos, G.E.; Papadakis, J.A.; Ganotakis, E.S.; Paraskevas, K.I.; Gazi, I.F.; Tzanakis, N.; Nair, D.R.; Mikhailidis, D.P., 2007:
Predicting coronary heart disease risk using the Framingham and PROCAM equations in dyslipidaemic patients without overt vascular disease

Zhang, D.; Song, X.; Lv, S.; Li, D.; Yan, S.; Zhang, M., 2016:
Predicting coronary no-reflow in patients with acute ST-segment elevation myocardial infarction using Bayesian approaches

Jaffe, R., 2013:
Predicting coronary no-reflow in patients with acute myocardial infarction

Hsu, J-Ting.; Chen, Y-Ju.; Tsai, M-Tzu.; Lan, H.Haw-Chang.; Cheng, F-Chou.; Chen, M.Y.C.; Wang, S-Ping., 2013:
Predicting cortical bone strength from DXA and dental cone-beam CT

Dummer, J.F.; Epton, M.J.; Cowan, J.O.; Cook, J.M.; Condliffe, R.; Landhuis, C.Erik.; Smith, A.D.; Taylor, D.Robin., 2009:
Predicting corticosteroid response in chronic obstructive pulmonary disease using exhaled nitric oxide

Armbruster, D.; Mueller, A.; Strobel, A.; Lesch, K-Peter.; Brocke, B.; Kirschbaum, C., 2011:
Predicting cortisol stress responses in older individuals: influence of serotonin receptor 1A gene (HTR1A) and stressful life events

Perkins, J.D., 2008:
Predicting cost in liver transplantation

Chang, H-Yen.; Weiner, J.P.; Richards, T.M.; Bleich, S.N.; Segal, J.B., 2012:
Predicting costs with diabetes complications severity index in claims data

Yu, B., 2014:
Predicting county-level cancer incidence rates and counts in the USA

Tao, R.; Jiang, Z.; Yu, W.; Wang, J., 2014:
Predicting coupling specificity of GPCRs based on the optimization of the coupling regions

Kang, T.Wook.; Rhim, H.; Lee, M.Woo.; Kim, W.; Park, J.Gu., 2014:
Predicting coverage of transverse subcostal sonography with the use of previous computed tomography before a sonographic liver examination: a prospective study

Xu, C.; Tarko, A.P.; Wang, W.; Liu, P., 2013:
Predicting crash likelihood and severity on freeways with real-time loop detector data

Minor, K.S.; Firmin, R.L.; Bonfils, K.A.; Chun, C.A.; Buckner, J.D.; Cohen, A.S., 2015:
Predicting creativity: the role of psychometric schizotypy and cannabis use in divergent thinking

Mu, Y.; Wu, F.; Chen, C.; Liu, Y.; Zhao, X.; Haiqing Liao; Giesy, J.P., 2014:
Predicting criteria continuous concentrations of 34 metals or metalloids by use of quantitative ion character-activity relationships-species sensitivity distributions (QICAR-SSD) model

Scheff, J.D.; Calvano, S.E.; Androulakis, I.P., 2014:
Predicting critical transitions in a model of systemic inflammation

Kwasniok, F., 2013:
Predicting critical transitions in dynamical systems from time series using nonstationary probability density modeling

Larremore, D.B.; Shew, W.L.; Restrepo, J.G., 2011:
Predicting criticality and dynamic range in complex networks: effects of topology

Bali, D.S.; Goldstein, J.L.; Banugaria, S.; Dai, J.; Mackey, J.; Rehder, C.; Kishnani, P.S., 2012:
Predicting cross-reactive immunological material (CRIM) status in Pompe disease using GAA mutations: lessons learned from 10 years of clinical laboratory testing experience

Hart, J.N.; Allan, N.L.; Claeyssens, F., 2010:
Predicting crystal structures ab initio: group 14 nitrides and phosphides

Price, S.L., 2013:
Predicting crystal structures of organic compounds

Vissers, T.; Preisler, Z.; Smallenburg, F.; Dijkstra, M.; Sciortino, F., 2013:
Predicting crystals of Janus colloids

Dahal, M.; Kumar, P.; Singh, G.K.; Arora, S.S.; Singh, M.P., 2008:
Predicting cubitus varus in supracondylar fractures of the humerus by Baumann's angles in post reduction X-rays

Babcock, A.H.; White, B.J.; Renter, D.G.; Dubnicka, S.R.; Scott, H.Morgan., 2014:
Predicting cumulative risk of bovine respiratory disease complex (BRDC) using feedlot arrival data and daily morbidity and mortality counts

Broennimann, O.; Guisan, A., 2008:
Predicting current and future biological invasions: both native and invaded ranges matter

Duff, K.; Dennett, K.; Tometich, D., 2012:
Predicting current memory with the modified telephone interview for cognitive status

Vandenbulcke, Gégory.; Thomas, I.; Int Panis, L., 2014:
Predicting cycling accident risk in Brussels: a spatial case-control approach

Lamberts, R.P., 2014:
Predicting cycling performance in trained to elite male and female cyclists

Waltenberger, B.; Schuster, D.; Paramapojn, S.; Gritsanapan, W.; Wolber, G.; Rollinger, J.M.; Stuppner, H., 2011:
Predicting cyclooxygenase inhibition by three-dimensional pharmacophoric profiling. Part II: Identification of enzyme inhibitors from Prasaplai, a Thai traditional medicine

Frigault, M.J.; June, C.H., 2012:
Predicting cytokine storms: it's about density

Rivet, C.A.; Hill, A.S.; Lu, H.; Kemp, M.L., 2011:
Predicting cytotoxic T-cell age from multivariate analysis of static and dynamic biomarkers

Langdon, S.R.; Mulgrew, J.; Paolini, G.V.; van Hoorn, W.P., 2010:
Predicting cytotoxicity from heterogeneous data sources with Bayesian learning

van Gestel, A.J.R.; Clarenbach, C.F.; Stöwhas, A.C.; Rossi, V.A.; Sievi, N.A.; Camen, G.; Russi, E.W.; Kohler, M., 2013:
Predicting daily physical activity in patients with chronic obstructive pulmonary disease

Csépe, Zán.; Makra, László.; Voukantsis, D.; Matyasovszky, Ián.; Tusnády, Gábor.; Karatzas, K.; Thibaudon, M., 2014:
Predicting daily ragweed pollen concentrations using Computational Intelligence techniques over two heavily polluted areas in Europe

Black, K.A.; McCloskey, K.A., 2014:
Predicting date rape perceptions: the effects of gender, gender role attitudes, and victim resistance

Sue, D.Y., 2010:
Predicting dead space ventilation in critically ill patients using clinically available data

Rehman, M.F.; Siddiqui, M.Salman., 2014:
Predicting death and disability, is it really possible? A medical ICU prognostication model study

Sarkar, S.; Bhagat, I.; Dechert, R.E.; Barks, J.D., 2010:
Predicting death despite therapeutic hypothermia in infants with hypoxic-ischaemic encephalopathy

Brook, L.; Hain, R., 2008:
Predicting death in children

Bont, J.; Hak, E.; Hoes, A.W.; Macfarlane, J.T.; Verheij, T.J.M., 2008:
Predicting death in elderly patients with community-acquired pneumonia: a prospective validation study reevaluating the CRB-65 severity assessment tool

Mueller, L.D.; Shahrestani, P.; Rauser, C.L., 2011:
Predicting death in female Drosophila

Colice, G.L., 2012:
Predicting death in massive hemoptysis

Anaya, D.A.; Bulger, E.M.; Kwon, Y.S.; Kao, L.S.; Evans, H.; Nathens, A.B., 2010:
Predicting death in necrotizing soft tissue infections: a clinical score

Voigt, M.D., 2012 :
Predicting death in patients with acetaminophen-induced acute liver failure: the King's College Hospital model is on the SOFA, not the mat

Hollmann, M.; Rieger, J.W.; Baecke, S.; Lützkendorf, R.; Müller, C.; Adolf, D.; Bernarding, J., 2012:
Predicting decisions in human social interactions using real-time fMRI and pattern classification

Belleville, S.; Gauthier, S.; Lepage, E.; Kergoat, M-Jeanne.; Gilbert, B., 2015:
Predicting decline in mild cognitive impairment: a prospective cognitive study

Rompré, G.; Robinson, W.Douglas.; Desrochers, Aé.; Angehr, G., 2009:
Predicting declines in avian species richness under nonrandom patterns of habitat loss in a neotropical landscape

Grace, R.C.; Kivell, B.M.; Laugesen, M., 2016:
Predicting decreases in smoking with a cigarette purchase task: evidence from an excise tax rise in New Zealand

Chan, W-Shian.; Lee, A.; Spencer, F.A.; Crowther, M.; Rodger, M.; Ramsay, T.; Ginsberg, J.S., 2009:
Predicting deep venous thrombosis in pregnancy: out in "LEFt" field?

Found, R.; Boyce, M.S., 2012:
Predicting deer-vehicle collisions in an urban area

Suzuki, G.; Leon, L.Joshua.; Kimber, S.; Vigmond, E.J., 2010:
Predicting defibrillation outcome based on phase of ventricular activity during ICD implantation

Strohmenger, H-Ulrich., 2008:
Predicting defibrillation success

Beique, L.C.; Kline, G.A.; Dalton, B.; Duggan, K.; Yilmaz, S., 2013:
Predicting deficiency of vitamin D in renal transplant recipients in northern climates

Bruno-Soares, A.Martins.; Cadima, J.; Matos, T.de.Jesus.S., 2010:
Predicting degradability parameters of diets for ruminants using regressions on chemical components

Pogue-Geile, K.L.; Kim, C.; Jeong, J-Hyeon.; Tanaka, N.; Bandos, H.; Gavin, P.G.; Fumagalli, D.; Goldstein, L.C.; Sneige, N.; Burandt, E.; Taniyama, Y.; Bohn, O.L.; Lee, A.; Kim, S-Il.; Reilly, M.L.; Remillard, M.Y.; Blackmon, N.L.; Kim, S-Rim.; Horne, Z.D.; Rastogi, P.; Fehrenbacher, L.; Romond, E.H.; Swain, S.M.; Mamounas, E.P.; Wickerham, D.Lawrence.; Geyer, C.E.; Costantino, J.P.; Wolmark, N.; Paik, S., 2014:
Predicting degree of benefit from adjuvant trastuzumab in NSABP trial B-31

Mulhall, J.P.; Alex, B.; Choi, J.M., 2011:
Predicting delay in presentation in men with Peyronie's disease

Hernández, D.; Rufino, M.; González-Posada, Jé.Manuel.; Estupiñán, S.; Pérez, Gán.; Marrero-Miranda, D.; Torres, A.; Pascual, J., 2008:
Predicting delayed graft function and mortality in kidney transplantation

Nakagami, G.; Sanada, H.; Iizaka, S.; Kadono, T.; Higashino, T.; Koyanagi, H.; Haga, N., 2011:
Predicting delayed pressure ulcer healing using thermography: a prospective cohort study

Qin, W.; Li, Y.; Li, J.; Yu, L.; Wu, D.; Jing, R.; Pu, X.; Guo, Y.; Li, M., 2012:
Predicting deleterious non-synonymous single nucleotide polymorphisms in signal peptides based on hybrid sequence attributes

O'Connor, R.C.; Rasmussen, S.; Hawton, K., 2009:
Predicting deliberate self-harm in adolescents: a six month prospective study

Makin, S.D.J.; Wardlaw, J., 2014:
Predicting delirium after a stroke

Ebell, M.H., 2007:
Predicting delirium in hospitalized older patients

Weiselberg, R.S.; Su, M.K.; Greller, H.A., 2011:
Predicting delirium tremens

Olde Dubbelink, K.T.E.; Hillebrand, A.; Twisk, J.W.R.; Deijen, J.Berend.; Stoffers, D.; Schmand, B.A.; Stam, C.J.; Berendse, H.W., 2014:
Predicting dementia in Parkinson disease by combining neurophysiologic and cognitive markers

Laks, J., 2013:
Predicting dementia or diagnosing early stages of Alzheimer's disease? How the hippocampal volume and the Clinical Dementia Rating-SB can help early diagnosis

Barnes, D.E.; Yaffe, K., 2010:
Predicting dementia: role of dementia risk indices

Park, S.E.; D.S.lva, J.D.; Barnes, J.L.; Susarla, S.M.; Howell, T.H., 2010:
Predicting dental school performance based on prior dental experience and exposure

Moubayed, S.P.; Sampalis, J.S.; Ayad, T.; Guertin, L.; Bissada, E.; Gologan, O.E.; Soulières, D.; Lambert, L.; Filion, E.; Nguyen-Tan, P.Felix.; Christopoulos, A., 2015:
Predicting depression and quality of life among long-term head and neck cancer survivors

Lu, Q.; Bi, K.; Liu, C.; Luo, G.; Tang, H.; Yao, Z., 2014:
Predicting depression based on dynamic regional connectivity: a windowed Granger causality analysis of MEG recordings

Wolfe, F.; Michaud, K., 2009:
Predicting depression in rheumatoid arthritis: the signal importance of pain extent and fatigue, and comorbidity

Berman, M.I.; Hegel, M.T., 2015:
Predicting depression outcome in mental health treatment: a recursive partitioning analysis

O'Connor, R.C.; Rasmussen, S.; Hawton, K., 2010:
Predicting depression, anxiety and self-harm in adolescents: the role of perfectionism and acute life stress

Dingle, K.; Clavarino, A.; Williams, G.M.; Bor, W.; Najman, J.M.; Alati, R., 2012:
Predicting depressive and anxiety disorders with the YASR internalising scales (empirical and DSM-oriented)

Demirtepe-Saygılı, D.; Bozo, O., 2011:
Predicting depressive symptoms among the mothers of children with leukaemia: a caregiver stress model perspective

Mann, J.R.; McKeown, R.E.; Bacon, J.; Vesselinov, R.; Bush, F., 2008:
Predicting depressive symptoms and grief after pregnancy loss

Graven, L.J.; Grant, J.S.; Vance, D.E.; Pryor, E.R.; Grubbs, L.; Karioth, S., 2015:
Predicting depressive symptoms and self-care in patients with heart failure

Lee, H-Hua.; Friedlander, M.L., 2015:
Predicting depressive symptoms from acculturative family distancing: A study of Taiwanese parachute kids in adulthood

Gong, M.; Zhang, Y.; Weschler, C.J., 2015:
Predicting dermal absorption of gas-phase chemicals: transient model development, evaluation, and application

Zhang, S.; Garcia-D'Angeli, A.; Brennan, J.P.; Huo, Q., 2014:
Predicting detection limits of enzyme-linked immunosorbent assay (ELISA) and bioanalytical techniques in general

Vella, M.; Robinson, D.; Cardozo, L.; Srikrishna, S.; Cartwright, R., 2008:
Predicting detrusor overactivity using a physician-based scoring system

Quanjer, P.H.; Steenbruggen, I.; Ruppel, G.; Swanney, M.P., 2008:
Predicting development and progression of COPD

Pauly, R.P.; Tonelli, M., 2011:
Predicting development of CKD in the general population--early days in a rapidly evolving field

Harris Nwanyanwu, K.; Talwar, N.; Gardner, T.W.; Wrobel, J.S.; Herman, W.H.; Stein, J.D., 2013:
Predicting development of proliferative diabetic retinopathy

Kuhlman, K.R.; Olson, S.L.; Lopez-Duran, N.L., 2015:
Predicting developmental changes in internalizing symptoms: examining the interplay between parenting and neuroendocrine stress reactivity

Gray, K.E.; Kapp-Simon, K.A.; Starr, J.R.; Collett, B.R.; Wallace, E.R.; Speltz, M.L., 2015:
Predicting developmental delay in a longitudinal cohort of preschool children with single-suture craniosynostosis: is neurobehavioral assessment important?

Soleimani, F.; Teymouri, R.; Biglarian, A., 2013:
Predicting developmental disorder in infants using an artificial neural network

Kirton, A., 2013:
Predicting developmental plasticity after perinatal stroke

Macari, S.L.; Campbell, D.; Gengoux, G.W.; Saulnier, C.A.; Klin, A.J.; Chawarska, K., 2013:
Predicting developmental status from 12 to 24 months in infants at risk for Autism Spectrum Disorder: a preliminary report

Daston, G.P.; Naciff, J.M., 2010:
Predicting developmental toxicity through toxicogenomics

Fisher, L.; Mullan, J.T.; Skaff, M.M.; Glasgow, R.E.; Arean, P.; Hessler, D., 2010:
Predicting diabetes distress in patients with Type 2 diabetes: a longitudinal study

Vella, A.; Zinsmeister, A.R., 2012:
Predicting diabetes using measures of β-cell function

Balkau, B.; Lange, Céline.; Fezeu, L.; Tichet, J.; de Lauzon-Guillain, B.; Czernichow, S.; Fumeron, F.; Froguel, P.; Vaxillaire, M.; Cauchi, S.; Ducimetière, P.; Eschwège, E., 2008:
Predicting diabetes: clinical, biological, and genetic approaches: data from the Epidemiological Study on the Insulin Resistance Syndrome (DESIR)

Dagogo-Jack, S., 2012:
Predicting diabetes: our relentless quest for genomic nuggets

Gilhotra, Y.; Porter, P., 2007:
Predicting diabetic ketoacidosis in children by measuring end-tidal CO2 via non-invasive nasal capnography

Blech, I.; Katzenellenbogen, M.; Katzenellenbogen, A.; Wainstein, J.; Rubinstein, A.; Harman-Boehm, I.; Cohen, J.; Pollin, T.I.; Glaser, B., 2011:
Predicting diabetic nephropathy using a multifactorial genetic model

Teeters, M.; Bezila, D.; Benner, T.; Alfonso, P.; Alred, P., 2011:
Predicting diafiltration solution compositions for final ultrafiltration/diafiltration steps of monoclonal antibodies

Nardone, D.A., 2007:
Predicting diagnostic accuracy

Salvatore, P.; Baldessarini, R.J.; Khalsa, H-Mandir.K.; Amore, M.; D.V.ttorio, C.; Ferraro, G.; Maggini, C.; Tohen, M., 2013:
Predicting diagnostic change among patients diagnosed with first-episode DSM-IV-TR major depressive disorder with psychotic features

Voisin, S.; Pinto, F.; Morin-Ducote, G.; Hudson, K.B.; Tourassi, G.D., 2014 :
Predicting diagnostic error in radiology via eye-tracking and image analytics: preliminary investigation in mammography

Croteau, M-Noële.; Luoma, S.N., 2009:
Predicting dietborne metal toxicity from metal influxes

Yamaguchi, R.; Imoto, S.; Yamauchi, M.; Nagasaki, M.; Yoshida, R.; Shimamura, T.; Hatanaka, Y.; Ueno, K.; Higuchi, T.; Gotoh, N.; Miyano, S., 2009:
Predicting differences in gene regulatory systems by state space models

Bouffard, J.A., 2007:
Predicting differences in the perceived relevance of crime's costs and benefits in a test of rational choice theory

Rosario, M.; Schrimshaw, E.W.; Hunter, J., 2008:
Predicting different patterns of sexual identity development over time among lesbian, gay, and bisexual youths: a cluster analytic approach

Feder, M.T.; Blitstein, J.; Mason, B.; Hoenig, D.M., 2008:
Predicting differential renal function using computerized tomography measurements of renal parenchymal area

Shirgoska, B.; Netkovski, J., 2016:
Predicting difficult airway in apparently normal adult and pediatric patients

Lavi, R.; Segal, D.; Ziser, A., 2009:
Predicting difficult airways using the intubation difficulty scale: a study comparing obese and non-obese patients

Shah, K.H.; McGillicuddy, D.; Spear, J.; Edlow, J.A., 2007:
Predicting difficult and traumatic lumbar punctures

Bindra, A.; Prabhakar, H.; Bithal, P.K.; Singh, G.Pal.; Chowdhury, T., 2013:
Predicting difficult laryngoscopy in acromegalic patients undergoing surgery for excision of pituitary tumors: A comparison of extended Mallampati score with modified Mallampati classification

Sharma, D.; Prabhakar, H.; Bithal, P.K.; Ali, Z.; Singh, G.P.; Rath, G.P.; Dash, H.H., 2010:
Predicting difficult laryngoscopy in acromegaly: a comparison of upper lip bite test with modified Mallampati classification

Carron, M., 2015:
Predicting difficult mask ventilation: a crucial point of airway management in obese patients

Sharma, S.K.; Thapa, P.B.; Pandey, A.; Kayastha, B.; Poudyal, S.; Uprety, K.R.; Ranjit, S., 2008:
Predicting difficulties during laparoscopic cholecystectomy by preoperative ultrasound

Khoshrang, H.; Falahatkar, S.; Heidarzadeh, A.; Abad, M.; Rastjou Herfeh, N.; Naderi Nabi, B., 2014:
Predicting difficulty score for spinal anesthesia in transurethral lithotripsy surgery

Hsia, C.C.W.; Wagner, P.D.; Dane, D.Merrill.; Wagner, H.E.; Johnson, R.L., 2008:
Predicting diffusive alveolar oxygen transfer from carbon monoxide-diffusing capacity in exercising foxhounds

Wientjes, M.G.; Yeung, B.Z.; Lu, Z.; Wientjes, M.Guillaume.; Au, J.L.S., 2015:
Predicting diffusive transport of cationic liposomes in 3-dimensional tumor spheroids

Helles, G.; Fonseca, R., 2010:
Predicting dihedral angle probability distributions for protein coil residues from primary sequence using neural networks

Perugini, M.; Nuñez, E.Gabriel.Herrera.; Baldi, L.; Esposito, M.; Serpe, F.Paolo.; Amorena, M., 2013:
Predicting dioxin-like PCBs soil contamination levels using milk of grazing animal as indicator

Hill, A.M.; Gebo, K.; Hemmett, L.; Löthgren, M.; Allegri, G.; Smets, E., 2011:
Predicting direct costs of HIV care during the first year of darunavir-based highly active antiretroviral therapy using CD4 cell counts: evidence from POWER

Kim, E.Dh.; Sabharwal, A.; Vetta, A.R.; Blanchette, M., 2010:
Predicting direct protein interactions from affinity purification mass spectrometry data

Soyka, F.; Robuffo Giordano, P.; Beykirch, K.; Bülthoff, H.H., 2011:
Predicting direction detection thresholds for arbitrary translational acceleration profiles in the horizontal plane

Schoufour, J.D.; Mitnitski, A.; Rockwood, K.; Hilgenkamp, T.I.M.; Evenhuis, H.M.; Echteld, M.A., 2015:
Predicting disabilities in daily functioning in older people with intellectual disabilities using a frailty index

Cunningham, J.L.; Wernroth, L.; von Knorring, L.; Berglund, L.; Ekselius, L., 2013:
Predicting disagreement between physicians and patients on depression response and remission

Bersch, C., 2010:
Predicting disaster

Ross, T.; Querengässer, J.; Fontao, Mía.Isabel.; Hoffmann, K., 2012:
Predicting discharge in forensic psychiatry: the legal and psychosocial factors associated with long and short stays in forensic psychiatric hospitals

Sirois, M-Josée.; Lavoie, Aé.; Dionne, C.E., 2007:
Predicting discharge of trauma survivors to rehabilitation: a sampling frame solution for a population-based trauma-rehabilitation survey

Kanaan, S.F.; Yeh, H-Wen.; Waitman, R.L.; Burton, D.C.; Arnold, P.M.; Sharma, N.K., 2015:
Predicting discharge placement and health care needs after lumbar spine laminectomy

Szubski, C.R.; Tellez, A.; Klika, A.K.; Xu, M.; Kattan, M.W.; Guzman, J.A.; Barsoum, W.K., 2015 :
Predicting discharge to a long-term acute care hospital after admission to an intensive care unit

Basri, B.; Griffin, M.J., 2013:
Predicting discomfort from whole-body vertical vibration when sitting with an inclined backrest

Grenier, S.G.; Eger, T.R.; Dickey, J.P., 2010:
Predicting discomfort scores reported by LHD operators using whole-body vibration exposure values and musculoskeletal pain scores

Brown, J.L.; Sales, J.M.; DiClemente, R.J.; Salazar, L.F.; Vanable, P.A.; Carey, M.P.; Brown, L.K.; Romer, D.; Valois, R.F.; Stanton, B., 2012:
Predicting discordance between self-reports of sexual behavior and incident sexually transmitted infections with African American female adolescents: results from a 4-city study

Gravel, S., 2015:
Predicting discovery rates of genomic features

Sun, K.; Gonçalves, J.P.; Larminie, C.; Przulj, Nša., 2014:
Predicting disease associations via biological network analysis

Lee, S.; Lee, E.; Lee, K.H.; Lee, D., 2011:
Predicting disease phenotypes based on the molecular networks with condition-responsive correlation

Lidsky, M.E.; Turley, R.S.; Beasley, G.M.; Sharma, K.; Tyler, D.S., 2013:
Predicting disease progression after regional therapy for in-transit melanoma

Palmer, K.; Lupo, F.; Perri, R.; Salamone, G.; Fadda, L.; Caltagirone, C.; Musicco, M.; Cravello, L., 2012:
Predicting disease progression in Alzheimer's disease: the role of neuropsychiatric syndromes on functional and cognitive decline

Mallory, G.B., 2012:
Predicting disease progression in cystic fibrosis: new use of an old tool

Manor, O.; Segal, E., 2014:
Predicting disease risk using bootstrap ranking and classification algorithms

Khalilia, M.; Chakraborty, S.; Popescu, M., 2011:
Predicting disease risks from highly imbalanced data using random forest

Markel, T.A.; Engelstad, H.; Poindexter, B.B., 2014:
Predicting disease severity of necrotizing enterocolitis: how to identify infants for future novel therapies

Li, Y.; Wen, Z.; Xiao, J.; Yin, H.; Yu, L.; Yang, L.; Li, M., 2011:
Predicting disease-associated substitution of a single amino acid by analyzing residue interactions

Yang, L.; Zhao, X.; Tang, X., 2015:
Predicting disease-related proteins based on clique backbone in protein-protein interaction network

Gao, S.; Jia, S.; Hessner, M.J.; Wang, X., 2013:
Predicting disease-related subnetworks for type 1 diabetes using a new network activity score

Chen, B.; Westerhoff, P., 2010:
Predicting disinfection by-product formation potential in water

Kabir, M.; Lau, T.T.; Rodney, D.; Yip, S.; Van Vliet, K.J., 2010:
Predicting dislocation climb and creep from explicit atomistic details

Zou, X.; Liu, Y.; Yakobson, B.I., 2012:
Predicting dislocations and grain boundaries in two-dimensional metal-disulfides from the first principles

Han, P.; Zhang, X.; Feng, Z-Ping., 2009 :
Predicting disordered regions in proteins using the profiles of amino acid indices

Anakwe, R.E.; Jenkins, P.J.; Moran, M., 2011:
Predicting dissatisfaction after total hip arthroplasty: a study of 850 patients

Scott, C.E.H.; Howie, C.R.; MacDonald, D.; Biant, L.C., 2010:
Predicting dissatisfaction following total knee replacement: a prospective study of 1217 patients

Cross, W.; Cerulli, C.; Richards, H.; He, H.; Herrmann, J., 2012:
Predicting dissemination of a disaster mental health "Train-the-Trainer" program

Singh, K.P.; Gupta, S.; Rai, P., 2014:
Predicting dissolved oxygen concentration using kernel regression modeling approaches with nonlinear hydro-chemical data

Mann, K.A.; Lee, J.; Arrington, S.A.; Damron, T.A.; Allen, M.J., 2008:
Predicting distal femur bone strength in a murine model of tumor osteolysis

Burkhart, T.A.; Dunning, C.E.; Andrews, D.M., 2013:
Predicting distal radius bone strains and injury in response to impacts using multi-axial accelerometers

Arriagada, R.; Rutqvist, L-Erik.; Johansson, H.; Kramar, A.; Rotstein, S., 2008:
Predicting distant dissemination in patients with early breast cancer

Chen, J.; Wang, W.; Zhang, Y.; Chen, Y.; Hu, T., 2014:
Predicting distant metastasis and chemoresistance using plasma miRNAs

Gnant, M.; Filipits, M.; Greil, R.; Stoeger, H.; Rudas, M.; Bago-Horvath, Z.; Mlineritsch, B.; Kwasny, W.; Knauer, M.; Singer, C.; Jakesz, R.; Dubsky, P.; Fitzal, F.; Bartsch, R.; Steger, G.; Balic, M.; Ressler, S.; Cowens, J.W.; Storhoff, J.; Ferree, S.; Schaper, C.; Liu, S.; Fesl, C.; Nielsen, T.O.; Fohler, H.; Klucky, B.; Steiner, K.; Gao, D.; Barry, G.; Huntsman, D.G.; Parker, A.; Tsang, P., 2015:
Predicting distant recurrence in receptor-positive breast cancer patients with limited clinicopathological risk: using the PAM50 Risk of Recurrence score in 1478 postmenopausal patients of the ABCSG-8 trial treated with adjuvant endocrine therapy alone

Hosseinpour, B.; Bakhtiarizadeh, M.Reza.; Khosravi, P.; Ebrahimie, E., 2013:
Predicting distinct organization of transcription factor binding sites on the promoter regions: a new genome-based approach to expand human embryonic stem cell regulatory network

van Strien, T., 2010:
Predicting distress-induced eating with self-reports: mission impossible or a piece of cake?

Mweya, C.Nyamunura.; Kimera, S.Iddi.; Kija, J.Bukombe.; Mboera, L.E.G., 2013:
Predicting distribution of Aedes aegypti and Culex pipiens complex, potential vectors of Rift Valley fever virus in relation to disease epidemics in East Africa

Ramgopal, S.; Powell, C.; Zarowski, M.; Alexopoulos, A.V.; Kothare, S.V.; Loddenkemper, T., 2014:
Predicting diurnal and sleep/wake seizure patterns in paediatric patients of different ages

Norman, G.R.; Wenghofer, E.; Klass, D., 2008:
Predicting doctor performance outcomes of curriculum interventions: problem-based learning and continuing competence

González, A.J.; Liao, L., 2011:
Predicting domain-domain interaction based on domain profiles with feature selection and support vector machines

Lawyer, S.R.; Schoepflin, F.J., 2013:
Predicting domain-specific outcomes using delay and probability discounting for sexual versus monetary outcomes

Lenzenweger, M.F.; Clarkin, J.F.; Levy, K.N.; Yeomans, F.E.; Kernberg, O.F. , 2012:
Predicting domains and rates of change in borderline personality disorder

Pine, J.K.; Goldsmith, P.J.; Ridgway, D.M.; Pollard, S.G.; Menon, K.V.; Attia, M.; Ahmad, N., 2011:
Predicting donor asystole following withdrawal of treatment in donation after cardiac death

Uchida, H.; Takeuchi, H.; Graff-Guerrero, A.; Suzuki, T.; Watanabe, K.; Mamo, D.C., 2011:
Predicting dopamine D₂ receptor occupancy from plasma levels of antipsychotic drugs: a systematic review and pooled analysis

Navailles, S.; D.G.ovanni, G.; D.D.urwaerdère, P., 2015:
Predicting dopaminergic effects of L-DOPA in the treatment for Parkinson's disease

Hagihara, M.; Kuti, J.L.; Nicolau, D.P., 2012:
Predicting doripenem susceptibility based on meropenem and imipenem interpretation for Pseudomonas aeruginosa

Appenzoller, L.M.; Michalski, J.M.; Thorstad, W.L.; Mutic, S.; Moore, K.L., 2013:
Predicting dose-volume histograms for organs-at-risk in IMRT planning

Shibeko, A.M.; Woodle, S.A.; Mahmood, I.; Jain, N.; Ovanesov, M.V., 2015:
Predicting dosing advantages of factor VIIa variants with altered tissue factor-dependent and lipid-dependent activities

Liu, C.C.; Hosking, S.G.; Lenné, M.G., 2010:
Predicting driver drowsiness using vehicle measures: recent insights and future challenges

Curtin, E.; Langlois, N.E.I., 2007:
Predicting driver from front passenger using only the postmortem pattern of injury following a motor vehicle collision

Hines, A.; Bundy, A.C., 2015:
Predicting driving ability using DriveSafe and DriveAware in people with cognitive impairments: a replication study

LaBrie, J.W.; Napper, L.E.; Ghaidarov, T.M., 2013:
Predicting driving after drinking over time among college students: the emerging role of injunctive normative perceptions

Brookings, J.B.; DeRoo, H.; Grimone, J., 2009:
Predicting driving anger from trait aggression and self-control

Bédard, M.; Weaver, B.; Darzins, P.; Porter, M.M., 2008:
Predicting driving performance in older adults: we are not there yet!

Huppelschoten, A.G.; van Dongen, A.J.C.M.; Philipse, I.C.P.; Hamilton, C.J.C.M.; Verhaak, C.M.; Nelen, W.L.D.M.; Kremer, J.A.M., 2014:
Predicting dropout in fertility care: a longitudinal study on patient-centredness

Deane, F.P.; Wootton, D.J.; Hsu, C-I.; Kelly, P.J., 2012:
Predicting dropout in the first 3 months of 12-step residential drug and alcohol treatment in an Australian sample

Croft, M.; Keely, B.; Morris, I.; Tann, L.; Lappin, G., 2012:
Predicting drug candidate victims of drug-drug interactions, using microdosing

Wu, L.J.; Altshuler, S.J.; Short, R.A.; Roll, J.M., 2012:
Predicting drug court outcome among amphetamine-using participants

Mattson, C.; Powers, B.; Halfaker, D.; Akeson, S.; Ben-Porath, Y., 2014:
Predicting drug court treatment completion using the MMPI-2-RF

Benet, L.Z., 2010:
Predicting drug disposition via application of a Biopharmaceutics Drug Disposition Classification System

Goh, W.Yee.; Lim, C.Peng.; Peh, K.Khiang., 2008:
Predicting drug dissolution profiles with an ensemble of boosted neural networks: a time series approach

Hirashima, Y.; Shirao, K., 2013:
Predicting drug efficacy-fluorinated pyrimidines (fluorouracil, S-1 and capecitabine)

Naversnik, K.; Bohanec, S., 2008:
Predicting drug hydrolysis based on moisture uptake in various packaging designs

Pichler, W.J., 2009:
Predicting drug hypersensitivity by in vitro tests

Pragyan, P.; Kesharwani, S.S.; Nandekar, P.P.; Rathod, V.; Sangamwar, A.T., 2015:
Predicting drug metabolism by CYP1A1, CYP1A2, and CYP1B1: insights from MetaSite, molecular docking and quantum chemical calculations

Rydberg, P.; Olsen, L., 2013:
Predicting drug metabolism by cytochrome P450 2C9: comparison with the 2D6 and 3A4 isoforms

Bolt, H.M.; Hengstler, J.G., 2009:
Predicting drug metabolism-dependent toxicity

D.V.ieze, M.; Lynen, Fédéric.; Chen, K.; Szucs, R.; Sandra, P., 2013:
Predicting drug penetration across the blood-brain barrier: comparison of micellar liquid chromatography and immobilized artificial membrane liquid chromatography

Sinek, J.P.; Sanga, S.; Zheng, X.; Frieboes, H.B.; Ferrari, M.; Cristini, V., 2008:
Predicting drug pharmacokinetics and effect in vascularized tumors using computer simulation

Siepmann, J.; Karrout, Y.; Gehrke, M.; Penz, F.K.; Siepmann, F., 2013 :
Predicting drug release from HPMC/lactose tablets

Hou, T.; Zhang, W.; Wang, J.; Wang, W., 2008:
Predicting drug resistance of the HIV-1 protease using molecular interaction energy components

Usary, J.; Zhao, W.; Darr, D.; Roberts, P.J.; Liu, M.; Balletta, L.; Karginova, O.; Jordan, J.; Combest, A.; Bridges, A.; Prat, A.; Cheang, M.C.U.; Herschkowitz, J.I.; Rosen, J.M.; Zamboni, W.; Sharpless, N.E.; Perou, C.M., 2014:
Predicting drug responsiveness in human cancers using genetically engineered mice

Pauwels, E.; Stoven, Véronique.; Yamanishi, Y., 2011:
Predicting drug side-effect profiles: a chemical fragment-based approach

Tatonetti, N.P.; Liu, T.; Altman, R.B., 2010:
Predicting drug side-effects by chemical systems biology

Lienard, P.; Gavartin, J.; Boccardi, G.; Meunier, M., 2015:
Predicting drug substances autoxidation

Sos, M.L.; Michel, K.; Zander, T.; Weiss, J.; Frommolt, P.; Peifer, M.; Li, D.; Ullrich, R.; Koker, M.; Fischer, F.; Shimamura, T.; Rauh, D.; Mermel, C.; Fischer, S.; Stückrath, I.; Heynck, S.; Beroukhim, R.; Lin, W.; Winckler, W.; Shah, K.; LaFramboise, T.; Moriarty, W.F.; Hanna, M.; Tolosi, L.; Rahnenführer, Jörg.; Verhaak, R.; Chiang, D.; Getz, G.; Hellmich, M.; Wolf, Jürgen.; Girard, L.; Peyton, M.; Weir, B.A.; Chen, T-Hsiu.; Greulich, H.; Barretina, J.; Shapiro, G.I.; Garraway, L.A.; Ga, 2009:
Predicting drug susceptibility of non-small cell lung cancers based on genetic lesions

Jerby, L.; Ruppin, E., 2013:
Predicting drug targets and biomarkers of cancer via genome-scale metabolic modeling

Wang, Y-Ying.; Nacher, J.C.; Zhao, X-Ming., 2012:
Predicting drug targets based on protein domains

Johnson, M.B.; Voas, R.A.; Miller, B.A.; Holder, H.D., 2009:
Predicting drug use at electronic music dance events: self-reports and biological measurement

Obach, R.Scott., 2009:
Predicting drug-drug interactions from in vitro drug metabolism data: challenges and recent advances

Tachibana, T.; Kato, M.; Takano, J.; Sugiyama, Y., 2011:
Predicting drug-drug interactions involving the inhibition of intestinal CYP3A4 and P-glycoprotein

Zhang, L.; Zhang, Y.Derek.; Zhao, P.; Huang, S-Mei., 2009:
Predicting drug-drug interactions: an FDA perspective

Yeo, K.Rowland.; Jamei, M.; Rostami-Hodjegan, A., 2013:
Predicting drug-drug interactions: application of physiologically based pharmacokinetic models under a systems biology approach

Townsend, C.; Brown, B.S., 2013:
Predicting drug-induced QT prolongation and torsades de pointes: a review of preclinical endpoint measures

Zhang, J.; Huan, J., 2014:
Predicting drug-induced QT prolongation effects using multi-view learning

Low, Y.; Uehara, T.; Minowa, Y.; Yamada, H.; Ohno, Y.; Urushidani, T.; Sedykh, A.; Muratov, E.; Kuz'min, V.; Fourches, D.; Zhu, H.; Rusyn, I.; Tropsha, A., 2011:
Predicting drug-induced hepatotoxicity using QSAR and toxicogenomics approaches

Lu, H.Rong.; Rohrbacher, J.; Vlaminckx, E.; Van Ammel, K.; Yan, G-Xin.; Gallacher, D.J., 2010:
Predicting drug-induced slowing of conduction and pro-arrhythmia: identifying the 'bad' sodium current blockers

Ishikita, H.; Warshel, A., 2007:
Predicting drug-resistant mutations of HIV protease

He, Z.; Zhang, J.; Shi, X-He.; Hu, L-Le.; Kong, X.; Cai, Y-Dong.; Chou, K-Chen., 2011:
Predicting drug-target interaction networks based on functional groups and biological features

Yu, W.; Yan, Y.; Liu, Q.; Wang, J.; Jiang, Z., 2014:
Predicting drug-target interaction networks of human diseases based on multiple feature information

Heiskanen, M.A.; Aittokallio, T., 2013:
Predicting drug-target interactions through integrative analysis of chemogenetic assays in yeast

Kim, S.; Jin, D.; Lee, H., 2014:
Predicting drug-target interactions using drug-drug interactions

Cobanoglu, M.Can.; Liu, C.; Hu, F.; Oltvai, Zán.N.; Bahar, I., 2014:
Predicting drug-target interactions using probabilistic matrix factorization

Fuller, J.C.; Burgoyne, N.J.; Jackson, R.M., 2008:
Predicting druggable binding sites at the protein-protein interface

González-Díaz, H.; Romaris, F.; Duardo-Sanchez, A.; Pérez-Montoto, Lázaro.G.; Prado-Prado, F.; Patlewicz, G.; Ubeira, F.M., 2011:
Predicting drugs and proteins in parasite infections with topological indices of complex networks: theoretical backgrounds, applications, and legal issues

Chen, L.; Huang, T.; Zhang, J.; Zheng, M-Yue.; Feng, K-Yan.; Cai, Y-Dong.; Chou, K-Chen., 2014:
Predicting drugs side effects based on chemical-chemical interactions and protein-chemical interactions

Szczesna, D.H.; Alonso-Caneiro, D.; Iskander, D.Robert.; Read, S.A.; Collins, M.J., 2011:
Predicting dry eye using noninvasive techniques of tear film surface assessment

Anele, U.Y.; Domby, E.M.; Galyean, M.L., 2015:
Predicting dry matter intake by growing and finishing beef cattle: evaluation of current methods and equation development

McHugh, P.P.; Shah, S.H.; Johnston, T.D.; Gedaly, R.; Ranjan, D., 2010:
Predicting dry weight in patients with ascites and liver cirrhosis using computed tomography imaging

Wang, Y.; Ge, H.; Li, Y.; Xie, Y.; He, Y.; Xu, M.; Gu, Q.; Xu, J., 2015:
Predicting dual-targeting anti-influenza agents using multi-models

Quach, H.; Mileshkin, L.; Seymour, J.F.; Milner, A.; Ritchie, D.; Harrison, S.; Westerman, D.; Prince, H.Miles., 2009:
Predicting durable remissions following thalidomide therapy for relapsed myeloma

Cattalini, M.; Maduskuie, V.; Fawcett, P.T.; Brescia, A.M.; Rosé, C.D., 2009:
Predicting duration of beneficial effect of joint injection among children with chronic arthritis by measuring biomarkers concentration in synovial fluid at the time of injection

Shen, C-Hao.; Peng, C-Kan.; Chou, Y-Ching.; Pan, K-Ting.; Chang, S-Cheng.; Chang, S-Yueh.; Huang, K-Lun., 2015:
Predicting duration of mechanical ventilation in patients with carbon monoxide poisoning: a retrospective study

Hunt, M.A.; Bennell, K.L., 2011:
Predicting dynamic knee joint load with clinical measures in people with medial knee osteoarthritis

Zhu, F.; Guan, Y., 2014:
Predicting dynamic signaling network response under unseen perturbations

Ostafin, B.D.; Kassman, K.T.; de Jong, P.J.; van Hemel-Ruiter, M.E., 2015:
Predicting dyscontrolled drinking with implicit and explicit measures of alcohol attitude

Tahrani, A.A.; Geen, J.; Hanna, F.W.F.; Jones, P.W.; Cassidy, D.; Bates, D.; Fryer, A.A., 2011:
Predicting dysglycaemia in patients under investigation for acute coronary syndrome

Correa-Gallego, C.; Do, R.; Lafemina, J.; Gonen, M.; D'Angelica, M.I.; DeMatteo, R.P.; Fong, Y.; Kingham, T.Peter.; Brennan, M.F.; Jarnagin, W.R.; Allen, P.J., 2014:
Predicting dysplasia and invasive carcinoma in intraductal papillary mucinous neoplasms of the pancreas: development of a preoperative nomogram

Gonçalves, A.C.Pieroni.; Silva, L.Nunes.; Gebrim, Eísa.M.M.S.; Matayoshi, S.; Monteiro, Mário.Luiz.Ribeiro., 2013:
Predicting dysthyroid optic neuropathy using computed tomography volumetric analyses of orbital structures

Newton, S.E.; Smith, L.H.; Moore, G.; Magnan, M., 2007:
Predicting early academic achievement in a baccalaureate nursing program

Knauss, P.J.; Willson, P., 2013:
Predicting early academic success: HESI Admissions Assessment Exam

Wagener, G.; Raffel, B.; Young, A.T.; Minhaz, M.; Emond, J., 2013:
Predicting early allograft failure and mortality after liver transplantation: the role of the postoperative model for end-stage liver disease score

D.C.pua, M.; Livia Burzo, M.; D.P.lo, M.; Marino, A.; D.L.renzo, G.; Mesolella, M.; Mormile, R.; D.M.nno, G.; Cerbone, A.M., 2015:
Predicting early and delayed bleedings in children who undergo adeno-tonsillectomy surgery. Is it really possible?

Goodman, D.; Kasner, S.E.; Park, S., 2013:
Predicting early awakening from coma after intracerebral hemorrhage

E.R.ssi, F.; Bergsagel, J.D.; Arellano, M.; Gaddh, M.; Jillella, A.; Kota, V.; Heffner, L.T.; Winton, E.F.; Khoury, H.Jean., 2015:
Predicting early blast transformation in chronic-phase chronic myeloid leukemia: is immunophenotyping the missing link?

Matsumoto, S.; Takayama, T.; Wakatsuki, K.; Enomoto, K.; Tanaka, T.; Migita, K.; Ito, M.; Nakajima, Y., 2013:
Predicting early cancer-related deaths after curative esophagectomy for esophageal cancer

Yang, T-I.J.; Aukema, T.S.; van Tinteren, H.; Burgers, S.; Valdés Olmos, R.; Verheij, M., 2010:
Predicting early chemotherapy response with technetium-99m methoxyisobutylisonitrile SPECT/CT in advanced non-small cell lung cancer

Tokuda, Y.; Song, M-Ho.; Ueda, Y.; Usui, A.; Akita, T., 2007:
Predicting early coronary artery bypass graft failure by intraoperative transit time flow measurement

Sanon, S.; Lee, V-Vei.; Elayda, M.A.; Gondi, S.; Livesay, J.J.; Reul, G.J.; Wilson, J.M., 2013:
Predicting early death after cardiovascular surgery by using the Texas Heart Institute Risk Scoring Technique (THIRST)

Perel, P.; Prieto-Merino, D.; Shakur, H.; Clayton, T.; Lecky, F.; Bouamra, O.; Russell, R.; Faulkner, M.; Steyerberg, E.W.; Roberts, I., 2012:
Predicting early death in patients with traumatic bleeding: development and validation of prognostic model

Moore, A.R.; Shan, W.Li.Pi.; Hatzakorzian, R., 2013:
Predicting early epidurals: association of maternal, labor, and neonatal characteristics with epidural analgesia initiation at a cervical dilation of 3 cm or less

Vitaro, F.; Wanner, B., 2011:
Predicting early gambling in children

Scholes-Balog, K.Elizabeth.; Hemphill, S.; Reid, S.; Patton, G.; Toumbourou, J., 2013 :
Predicting early initiation of alcohol use: a prospective study of Australian children

Cao, X-lin.; Li, H.; Yu, X-ling.; Liang, P.; Dong, B-wei.; Fan, J.; Li, M.; Liu, F-yi., 2014:
Predicting early intrahepatic recurrence of hepatocellular carcinoma after microwave ablation using SELDI-TOF proteomic signature

Varghese, R.; Itagaki, S.; Anyanwu, A.C.; Milla, F.; Adams, D.H., 2014:
Predicting early left ventricular dysfunction after mitral valve reconstruction: the effect of atrial fibrillation and pulmonary hypertension

Augustin, S.; Muntaner, L.; Altamirano, Jé.T.; González, A.; Saperas, E.; Dot, J.; Abu-Suboh, M.; Armengol, J.R.; Malagelada, J.R.; Esteban, R.; Guardia, J.; Genescà, J., 2010:
Predicting early mortality after acute variceal hemorrhage based on classification and regression tree analysis

Chang, C.L.; Sullivan, G.D.; Karalus, N.C.; Mills, G.D.; McLachlan, J.D.; Hancox, R.J., 2011:
Predicting early mortality in acute exacerbation of chronic obstructive pulmonary disease using the CURB65 score

Gerdin, M.; Roy, N.; Khajanchi, M.; Kumar, V.; Dharap, S.; Felländer-Tsai, L.; Petzold, M.; Bhoi, S.; Saha, M.Lal.; von Schreeb, J., 2015:
Predicting early mortality in adult trauma patients admitted to three public university hospitals in urban India: a prospective multicentre cohort study

Tiernan, K.; Foster, S.L.; Cunningham, P.B.; Brennan, P.; Whitmore, E., 2015:
Predicting early positive change in multisystemic therapy with youth exhibiting antisocial behaviors

Kevan, A.; Pammer, K., 2010:
Predicting early reading skills from pre-reading measures of dorsal stream functioning

Yamazaki, Y.; Shiroyanagi, Y.; Matsuno, D.; Nishi, M., 2009:
Predicting early recurrent urinary tract infection in pretoilet trained children with vesicoureteral reflux

Yang, W-rui.; Xiong, Y-yuan.; Zhang, L.; Jing, L-ping.; Zhou, K.; Peng, G-xin.; Li, Y.; Ye, L.; Li, Y.; Li, J-ping.; Fan, H-hui.; Song, L.; Zhao, X.; Zhang, F-kui., 2014:
Predicting early response to immunosuppressive therapy in severe aplastic anemia by soluble transferrin receptor assay

Schofield, H-Lise.T.; Bierman, K.L.; Heinrichs, B.; Nix, R.L.; Bierman, K.L.; Coie, J.D.; Dodge, K.A.; Greenberg, M.T.; Lochman, J.E.; McMahon, R.J.; Pinderhughes, E.E., 2008:
Predicting early sexual activity with behavior problems exhibited at school entry and in early adolescence

Cordewener, K.A.H.; Bosman, A.M.T.; Verhoeven, L., 2013:
Predicting early spelling difficulties in children with specific language impairment: a clinical perspective

Duckett, J.R.A.; Patil, A.; Papanikolaou, N.S., 2008:
Predicting early voiding dysfunction after tension-free vaginal tape

Holzer, T.L., 1994:
Predicting earthquake effects--learning from northridge and loma prieta

Zheng, Y.; Espiritu, P.; Hakky, T.; Jutras, K.; Spiess, P.E., 2015:
Predicting ease of perinephric fat dissection at time of open partial nephrectomy using preoperative fat density characteristics

Miller, S.D.; Litovsky, R.Y.; Kluender, K.R., 2009:
Predicting echo thresholds from speech onset characteristics

Raucci, F.J.; Hoke, T.R.; Gutgesell, H.P., 2015:
Predicting economic and medical outcomes based on risk adjustment for congenital heart surgery classification of pediatric cardiovascular surgical admissions

Reay-Jones, F.P.F.; Wilson, L.T.; Reagan, T.E.; Legendre, B.L.; Way, M.O., 2008:
Predicting economic losses from the continued spread of the Mexican rice borer (Lepidoptera: Crambidae)

McLaughlin, B.C.; Xu, C-Yuan.; Rastetter, E.B.; Griffin, K.L., 2015:
Predicting ecosystem carbon balance in a warming Arctic: the importance of long-term thermal acclimation potential and inhibitory effects of light on respiration

Medvigy, D.; Moorcroft, P.R., 2012:
Predicting ecosystem dynamics at regional scales: an evaluation of a terrestrial biosphere model for the forests of northeastern North America

Vervaeke, H.K.E.; Benschop, A.; van den Brink, W.; Korf, D.J., 2008:
Predicting ecstasy use among young people at risk: a prospective study of initially ecstasy-naive subjects

Supervie, V.; Blower, S., 2012:
Predicting effect of pre-exposure prophylaxis on HIV epidemics

E.S.lh, A.A.; Aldik, Z.; Alnabhan, M.; Grant, B., 2007:
Predicting effective continuous positive airway pressure in sleep apnea using an artificial neural network

Shah, K.; Kurien, A.; Mishra, S.; Ganpule, A.; Muthu, V.; Sabnis, R.B.; Desai, M., 2010:
Predicting effectiveness of extracorporeal shockwave lithotripsy by stone attenuation value

Huang, H-Wen.; Shih, T-Ching.; Liauh, C-Tsung., 2010:
Predicting effects of blood flow rate and size of vessels in a vasculature on hyperthermia treatments using computer simulation

Kemps, H.M.; Schep, G.; de Vries, W.R.; Schmikli, S.L.; Zonderland, M.L.; Thijssen, E.J.M.; Wijn, P.F.F.; Doevendans, P.A., 2008:
Predicting effects of exercise training in patients with heart failure secondary to ischemic or idiopathic dilated cardiomyopathy

Marcarelli, A.M.; Van Kirk, R.W.; Baxter, C.V., 2011:
Predicting effects of hydrologic alteration and climate change on ecosystem metabolism in a western U.S. river

Reikvam, Håkon.; Nepstad, I.; Tamburini, J., 2014:
Predicting effects of kinase inhibitor in therapy for myeloid malignancies - the challenges in capturing disease heterogeneity

Snoeck, E.; Hadziyannis, S.J.; Puoti, C.; Swain, M.G.; Berg, T.; Marcellin, P.; Zarski, J-Pierre.; Jorga, K.; Zeuzem, S., 2007:
Predicting efficacy and safety outcomes in patients with hepatitis C virus genotype 1 and persistently 'normal' alanine aminotransferase levels treated with peginterferon alpha-2a (40KD) plus ribavirin

Caraglia, M.; Santini, D.; Bronte, G.; Rizzo, S.; Sortino, G.; Rini, G.Battista.; D.F.de, G.; Russo, A., 2012:
Predicting efficacy and toxicity in the era of targeted therapy: focus on anti-EGFR and anti-VEGF molecules

Qiao, Y.; Ma, L., 2014:
Predicting efficacy of cancer cell killing under hypoxic conditions with single cell DNA damage assay

Iwasaki, M.; Hoshian, F.; Tsuji, T.; Hirose, N.; Matsumoto, T.; Kitatani, N.; Sugawara, K.; Usui, R.; Kuwata, H.; Sugizaki, K.; Kitamoto, Y.; Fujiwara, S.; Watanabe, K.; Hyo, T.; Kurose, T.; Seino, Y.; Yabe, D., 2012:
Predicting efficacy of dipeptidyl peptidase-4 inhibitors in patients with type 2 diabetes: Association of glycated hemoglobin reduction with serum eicosapentaenoic acid and docosahexaenoic acid levels

Ozog, D.M., 2011:
Predicting efficacy of fractional carbon dioxide laser treatment for resurfacing

Cauchon, N.; Turcotte, E.; Lecomte, R.; Hasséssian, H.M.; Lier, J.E.van., 2012:
Predicting efficacy of photodynamic therapy by real-time FDG-PET in a mouse tumour model

Sergi, F.; Krebs, H.Igo.; Groissier, B.; Rykman, A.; Guglielmelli, E.; Volpe, B.T.; Schaechter, J.D., 2012:
Predicting efficacy of robot-aided rehabilitation in chronic stroke patients using an MRI-compatible robotic device

Saghaei, M.; Shetabi, H.; Golparvar, M., 2012:
Predicting efficiency of post-induction mask ventilation based on demographic and anatomical factors

Marcondes, M.I.; Tedeschi, L.O.; Valadares Filho, S.C.; Gionbelli, M.P., 2014:
Predicting efficiency of use of metabolizable energy to net energy for gain and maintenance of Nellore cattle

Samuel, A.P.S.; Xu, J.; Raymond, K.N., 2009:
Predicting efficient antenna ligands for Tb(III) emission

Dolghih, E.; Jacobson, M.P., 2013:
Predicting efflux ratios and blood-brain barrier penetration from chemical structure: combining passive permeability with active efflux by P-glycoprotein

Bonato, M.; Malecki, I.A.; Rybnik-Trzaskowska, P.K.; Cornwallis, C.K.; Cloete, S.W.P., 2015:
Predicting ejaculate quality and libido in male ostriches: effect of season and age

Antonakis, J.; Dalgas, O., 2009:
Predicting elections: child's play!

Dermol, J.; Miklavčič, D., 2015:
Predicting electroporation of cells in an inhomogeneous electric field based on mathematical modeling and experimental CHO-cell permeabilization to propidium iodide determination

Tan, Z-Jie.; Chen, S-Jie., 2011:
Predicting electrostatic forces in RNA folding

Ruiz Ortiz, Mín.; Romo, Eías.; Mesa, D.; Delgado, Mónica.; Anguita, M.; López Granados, A.; Castillo, J.C.; Arizón, Jé.M.; Suárez de Lezo, Jé., 2008:
Predicting embolic events in patients with nonvalvular atrial fibrillation: evaluation of the CHADS2 score in a Mediterranean population

Malik, R.K.; Landis, G.S.; Sundick, S.; Cayne, N.; Marin, M.; Faries, P.L., 2010:
Predicting embolic potential during carotid angioplasty and stenting: analysis of captured particulate debris, ultrasound characteristics, and prior carotid endarterectomy

François, P.; Siggia, E.D., 2010:
Predicting embryonic patterning using mutual entropy fitness and in silico evolution

Peck, J.S.; Benneyan, J.C.; Nightingale, D.J.; Gaehde, S.A., 2013:
Predicting emergency department inpatient admissions to improve same-day patient flow

Chase, V.J.; Cohn, A.E.M.; Peterson, T.A.; Lavieri, M.S., 2012:
Predicting emergency department volume using forecasting methods to create a "surge response" for noncrisis events

Schonwetter, R.S.; Clark, L.D.; Leedy, S.A.; Quinn, M.Jo.; Azer, M.; Kim, S., 2008:
Predicting emergency room visits and hospitalizations among hospice patients with cardiac disease

Blandon, A.Y.; Calkins, S.D.; Keane, S.P., 2010:
Predicting emotional and social competence during early childhood from toddler risk and maternal behavior

Hayes, B.; Douglas, C.; Bonner, A., 2015:
Predicting emotional exhaustion among haemodialysis nurses: a structural equation model using Kanter's structural empowerment theory

Kendall, E.; Terry, D., 2009:
Predicting emotional well-being following traumatic brain injury: a test of mediated and moderated models

Jimmieson, N.L.; White, K.M., 2011:
Predicting employee intentions to support organizational change: an examination of identification processes during a re-brand

Honarmand, K.; Akbar, N.; Kou, N.; Feinstein, A., 2011:
Predicting employment status in multiple sclerosis patients: the utility of the MS functional composite

Murphy, S.A.; Haja Mydin, H.; Fatah, S.; Antunes, G., 2011:
Predicting end-of-life in patients with an exacerbation of COPD by routine clinical assessment

Ditto, P.H.; Clark, C.J., 2014:
Predicting end-of-life treatment preferences: perils and practicalities

Chang, C-Lin.; Jin, Z.; Cheng, A.C., 2009:
Predicting end-point locomotion from neuromuscular activities of people with spina bifida: a self-organizing and adaptive technique for future implantable and non-invasive neural prostheses

Kerstann, K.F.; Bouzyk, M.; Abramovitz, M.; Leyland-Jones, B., 2009:
Predicting endocrine responsiveness: novel biomarkers on the horizon

Ma, C.X.; Sanchez, C.G.; Ellis, M.J., 2009:
Predicting endocrine therapy responsiveness in breast cancer

Azimi, P.; Mohammadi, H.Reza., 2014:
Predicting endoscopic third ventriculostomy success in childhood hydrocephalus: an artificial neural network analysis

Luten, R.; Kahn, N.; Wears, R.; Kissoon, N., 2007:
Predicting endotracheal tube size by length in newborns

Verbyla, K.L.; Calus, M.P.L.; Mulder, H.A.; de Haas, Y.; Veerkamp, R.F., 2010:
Predicting energy balance for dairy cows using high-density single nucleotide polymorphism information

Choquette, Séphane.; Chuin, Aélie.; Lalancette, D-Alexandre.; Brochu, M.; Dionne, I.J., 2011:
Predicting energy expenditure in elders with the metabolic cost of activities

Subramaniam, A.; McPhee, M.; Nagappan, R., 2012:
Predicting energy expenditure in sepsis: Harris-Benedict and Schofield equations versus the Weir derivation

Hiremath, S.V.; Ding, D.; Farringdon, J.; Cooper, R.A., 2013:
Predicting energy expenditure of manual wheelchair users with spinal cord injury using a multisensor-based activity monitor

Conger, S.A.; Scott, S.N.; Bassett, D.R., 2015:
Predicting energy expenditure through hand rim propulsion power output in individuals who use wheelchairs

Brun-Lafleur, L.; Delaby, L.; Husson, F.; Faverdin, P., 2010:
Predicting energy x protein interaction on milk yield and milk composition in dairy cows

Brown, L.D.; Feinberg, M..E.; Kan, M.L., 2012:
Predicting engagement in a transition to parenthood program for couples

Horton, J.K.; Wilson, S.H., 2013:
Predicting enhanced cell killing through PARP inhibition

Dadzie, S.Kokou.; Brenner, H., 2013:
Predicting enhanced mass flow rates in gas microchannels using nonkinetic models

Zhu, Y.; Sun, L.; Chen, Z.; Whitaker, J.W.; Wang, T.; Wang, W., 2014:
Predicting enhancer transcription and activity from chromatin modifications

Zalla, T.; Labruyère, N.; Clément, Aélie.; Georgieff, N., 2010:
Predicting ensuing actions in children and adolescents with autism spectrum disorders

Zarkevich, N.A.; Johnson, D.D., 2008:
Predicting enthalpies of molecular substances: application to LiBH4

Verevkin, S.P., 2008:
Predicting enthalpy of vaporization of ionic liquids: a simple rule for a complex property

Patel, C.J.; Butte, A.J., 2010:
Predicting environmental chemical factors associated with disease-related gene expression data

Chunco, A.J.; Phimmachak, S.; Sivongxay, N.; Stuart, B.L., 2014:
Predicting environmental suitability for a rare and threatened species (Lao newt, Laotriton laoensis) using validated species distribution models

Volkamer, A.; Kuhn, D.; Rippmann, F.; Rarey, M., 2013:
Predicting enzymatic function from global binding site descriptors

Sammond, D.W.; Yarbrough, J.M.; Mansfield, E.; Bomble, Y.J.; Hobdey, S.E.; Decker, S.R.; Taylor, L.E.; Resch, M.G.; Bozell, J.J.; Himmel, M.E.; Vinzant, T.B.; Crowley, M.F., 2015:
Predicting enzyme adsorption to lignin films by calculating enzyme surface hydrophobicity

Kaul, P.; Banerjee, U.C., 2008:
Predicting enzyme behavior in nonconventional media: correlating nitrilase function with solvent properties

Wang, Y.; Hu, X.; Sun, L.; Feng, Z.; Song, H., 2014:
Predicting enzyme subclasses by using random forest with multicharacteristic parameters

Shi, R.; Hu, X., 2010:
Predicting enzyme subclasses by using support vector machine with composite vectors

Li, L.; Zhou, X.; Ching, W-Ki.; Wang, P., 2011 :
Predicting enzyme targets for cancer drugs by profiling human metabolic reactions in NCI-60 cell lines

Benny, B.V.; Patel, M.Yogesh., 2015:
Predicting epidural steroid injections with laboratory markers and imaging techniques

Moghim, N.; Corne, D.W., 2015:
Predicting epileptic seizures in advance

Shahidi Zandi, A.; Tafreshi, R.; Javidan, M.; Dumont, G.A., 2013:
Predicting epileptic seizures in scalp EEG based on a variational Bayesian Gaussian mixture model of zero-crossing intervals

Bruzzo, A.Alessia.; Gesierich, B.; Rubboli, G.; Vimal, R.Lakhan.Pandey., 2008:
Predicting epileptic seizures with a mental simulation task: a prospective study

Tanguay, T.Annette.; Jensen, L.; Johnston, C., 2007:
Predicting episodes of hypotension by continuous blood volume monitoring among critically ill patients in acute renal failure on intermittent hemodialysis

MacLean, R.C., 2010:
Predicting epistasis: an experimental test of metabolic control theory with bacterial transcription and translation

Goss, K-Uwe., 2011:
Predicting equilibrium sorption of neutral organic chemicals into various polymeric sorbents with COSMO-RS

Novak, I.; Smithers-Sheedy, H.; Morgan, C., 2012:
Predicting equipment needs of children with cerebral palsy using the Gross Motor Function Classification System: a cross-sectional study

Taylor, F.L.; Abern, M.R.; Levine, L.A., 2012:
Predicting erectile dysfunction following surgical correction of Peyronie's disease without inflatable penile prosthesis placement: vascular assessment and preoperative risk factors

Kilminster, S.; Müller, S.; Menon, M.; Joseph, J.V.; Ralph, D.J.; Patel, H.R.H., 2012:
Predicting erectile function outcome in men after radical prostatectomy for prostate cancer

Briganti, A.; Gallina, A.; Suardi, N.; Capitanio, U.; Tutolo, M.; Bianchi, M.; Passoni, Nò.; Salonia, A.; Colombo, R.; D.G.rolamo, V.; Guazzoni, G.; Rigatti, P.; Montorsi, F., 2011:
Predicting erectile function recovery after bilateral nerve sparing radical prostatectomy: a proposal of a novel preoperative risk stratification

Grimm, L.J.; Ghate, S.V.; Yoon, S.C.; Kuzmiak, C.M.; Kim, C.; Mazurowski, M.A., 2014:
Predicting error in detecting mammographic masses among radiology trainees using statistical models based on BI-RADS features

Ekman, M.; Derrfuss, J.; Tittgemeyer, M.; Fiebach, C.J., 2013:
Predicting errors from reconfiguration patterns in human brain networks

Palma, D.A.; Senan, S.; Oberije, C.; Belderbos, J.; de Dios, Núria.Rodríguez.; Bradley, J.D.; Barriger, R.Bryan.; Moreno-Jiménez, M.; Kim, T.Hyun.; Ramella, S.; Everitt, S.; Rengan, R.; Marks, L.B.; D.R.yck, K.; Warner, A.; Rodrigues, G., 2013:
Predicting esophagitis after chemoradiation therapy for non-small cell lung cancer: an individual patient data meta-analysis

Hwang, Y-Chii.; Lin, C-Ching.; Chang, J-Yun.; Mori, H.; Juan, H-Fen.; Huang, H-Cheng., 2010:
Predicting essential genes based on network and sequence analysis

Lu, Y.; Deng, J.; Rhodes, J.C.; Lu, H.; Lu, L.Jason., 2015:
Predicting essential genes for identifying potential drug targets in Aspergillus fumigatus

Song, K.; Tong, T.; Wu, F., 2014:
Predicting essential genes in prokaryotic genomes using a linear method: ZUPLS

Kretschmer, J.; Riedlinger, A.; Möller, K., 2013:
Predicting etCO2 Response in a Model of Ventilation-Perfusion Mismatch

Oswald, F.L.; Mitchell, G.; Blanton, H.; Jaccard, J.; Tetlock, P.E., 2014:
Predicting ethnic and racial discrimination: a meta-analysis of IAT criterion studies

Prevoo, Mëlle.J.L.; Malda, M.; Mesman, J.; Emmen, R.A.G.; Yeniad, N.; Van Ijzendoorn, M.H.; Linting, Mëlle., 2016:
Predicting ethnic minority children's vocabulary from socioeconomic status, maternal language and home reading input: different pathways for host and ethnic language

Wang, Y.; Zhang, X-Sun.; Xia, Y., 2009:
Predicting eukaryotic transcriptional cooperativity by Bayesian network integration of genome-wide data

Donovan, J.Mark.; Elliott, M.R.; Heitjan, D.F., 2007:
Predicting event times in clinical trials when randomization is masked and blocked

Razani, J.; Wong, J.T.; Dafaeeboini, N.; Edwards-Lee, T.; Lu, P.; Alessi, C.; Josephson, K., 2009:
Predicting everyday functional abilities of dementia patients with the Mini-Mental State Examination

Neher, R.A.; Russell, C.A.; Shraiman, B.I., 2015:
Predicting evolution from the shape of genealogical trees

Langerhans, R.Brian., 2011:
Predicting evolution with generalized models of divergent selection: a case study with poeciliid fish

Munday, P.L.; Warner, R.R.; Monro, K.; Pandolfi, J.M.; Marshall, D.J., 2015:
Predicting evolutionary responses to climate change in the sea

Shahmoradi, A.; Sydykova, D.K.; Spielman, S.J.; Jackson, E.L.; Dawson, E.T.; Meyer, A.G.; Wilke, C.O., 2015:
Predicting evolutionary site variability from structure in viral proteins: buriedness, packing, flexibility, and design

Gutekunst, D.J.; Patel, T.K.; Smith, K.E.; Commean, P.K.; Silva, M.J.; Sinacore, D.R., 2013:
Predicting ex vivo failure loads in human metatarsals using bone strength indices derived from volumetric quantitative computed tomography

Thompson, M.E.; Kohring, J.M.; McFann, K.; McNair, B.; Hansen, J.K.; Miller, N.H., 2015:
Predicting excessive hemorrhage in adolescent idiopathic scoliosis patients undergoing posterior spinal instrumentation and fusion

Rudra, I.; Wu, Q.; Van Voorhis, T., 2007:
Predicting exchange coupling constants in frustrated molecular magnets using density functional theory

Forhan, M.; Zagorski, B.M.; Marzonlini, S.; Oh, P.; Alter, D.A., 2015:
Predicting exercise adherence for patients with obesity and diabetes referred to a cardiac rehabilitation and secondary prevention program

Husebø, A.M.Lunde.; Dyrstad, S.M.; Søreide, J.A.; Bru, E., 2013:
Predicting exercise adherence in cancer patients and survivors: a systematic review and meta-analysis of motivational and behavioural factors

Kuspinar, A.; Andersen, R.E.; Teng, S.Yuan.; Asano, M.; Mayo, N.E., 2010:
Predicting exercise capacity through submaximal fitness tests in persons with multiple sclerosis

King, J.C.; Hines, O.Joe., 2010:
Predicting exocrine insufficiency following pancreatic resection

Okut, H.; Wu, X-Liao.; Rosa, G.J.M.; Bauck, S.; Woodward, B.W.; Schnabel, R.D.; Taylor, J.F.; Gianola, D., 2014:
Predicting expected progeny difference for marbling score in Angus cattle using artificial neural networks and Bayesian regression models

Smialowski, P.; Martin-Galiano, A.J.; Cox, Jürgen.; Frishman, D., 2007:
Predicting experimental properties of proteins from sequence by machine learning techniques

Vandereet, J.; Maes, B.; Lembrechts, D.; Zink, I., 2011:
Predicting expressive vocabulary acquisition in children with intellectual disabilities: a 2-year longitudinal study

Rodwell, T.C.; Valafar, F.; Douglas, J.; Qian, L.; Garfein, R.S.; Chawla, A.; Torres, J.; Zadorozhny, V.; Kim, M.Soo.; Hoshide, M.; Catanzaro, D.; Jackson, L.; Lin, G.; Desmond, E.; Rodrigues, C.; Eisenach, K.; Victor, T.C.; Ismail, N.; Crudu, V.; Gler, M.Tarcela.; Catanzaro, A., 2014:
Predicting extensively drug-resistant Mycobacterium tuberculosis phenotypes with genetic mutations

Heberle, A.E.; Krill, S.C.; Briggs-Gowan, M.J.; Carter, A.S., 2015:
Predicting externalizing and internalizing behavior in kindergarten: examining the buffering role of early social support

Kosten, T.A.; Meisch, R.A., 2014:
Predicting extinction and reinstatement of alcohol and sucrose self-administration in outbred rats

Leão, T.C.C.; Fonseca, C.R.; Peres, C.A.; Tabarelli, M., 2015:
Predicting extinction risk of Brazilian Atlantic forest angiosperms

Keith, D.A.; Akçakaya, H.Resit.; Thuiller, W.; Midgley, G.F.; Pearson, R.G.; Phillips, S.J.; Regan, H.M.; Araújo, M.B.; Rebelo, T.G., 2008:
Predicting extinction risks under climate change: coupling stochastic population models with dynamic bioclimatic habitat models

Garber, A.; Hallerberg, S.; Kantz, H., 2009:
Predicting extreme avalanches in self-organized critical sandpiles

Arthur, E.J.; Yesselman, J.D.; Brooks, C.L., 2012:
Predicting extreme pKa shifts in staphylococcal nuclease mutants with constant pH molecular dynamics

Mokhlesi, B.; Tulaimat, A.; Gluckman, T.J.; Wang, Y.; Evans, A.T.; Corbridge, T.C., 2007:
Predicting extubation failure after successful completion of a spontaneous breathing trial

Bilello, J.F.; Davis, J.W.; Cagle, K.M.; Kaups, K.L., 2013:
Predicting extubation failure in blunt trauma patients with pulmonary contusion

Hughes, G.; Waszak, F., 2015:
Predicting faces and houses: category-specific visual action-effect prediction modulates late stages of sensory processing

Preis, M.; Soudry, E.; Bachar, G.; Shufel, H.; Feinmesser, R.; Shpitzer, T., 2011:
Predicting facial nerve invasion by parotid gland carcinoma and outcome of facial reanimation

Steffen, A.M.; Jackson, C.S., 2012:
Predicting facilitators' behaviors during Alzheimer's family support group meetings

Hong, Y-Ho.; Kim, C-Hyun.; Che, G-Sung.; Lee, S-Hoon.; Ghang, C-Gu.; Choi, Y-Seok., 2011:
Predicting factors affecting clinical outcomes for saccular aneurysms of posterior inferior cerebellar artery with subarachnoid hemorrhage

Heydarian, F.; Ashrafzadeh, F.; Rostazadeh, A., 2015:
Predicting factors and prevalence of meningitis in patients with first seizure and fever aged 6 to 18 months

Song, Y.Hoon.; Shin, T.Gun.; Kang, M.Ju.; Sim, M.Seob.; Jo, I.Joon.; Song, K.Jeong.; Jeong, Y.Kwon., 2012:
Predicting factors associated with clinical deterioration of sepsis patients with intermediate levels of serum lactate

Towashiraporn, K.; Krittayaphong, R.; Yindeengam, A., 2013:
Predicting factors for a false positive treadmill exercise stress test

Lo, L-Wei.; Tai, C-Tai.; Lin, Y-Jiang.; Chang, S-Lin.; Udyavar, A.R.; Hu, Y-Feng.; Ueng, K-Chang.; Tsai, W-Chin.; Tuan, T-Chun.; Chang, C-Jung.; Kao, T.; Tsao, H-Ming.; Wongcharoen, W.; Higa, S.; Chen, S-Ann., 2009:
Predicting factors for atrial fibrillation acute termination during catheter ablation procedures: implications for catheter ablation strategy and long-term outcome

Viriyasiripong, S.; Akarasakul, D.; Thaidamrong, T.; Doungkae, S., 2013:
Predicting factors for biochemical recurrence and oncological outcomes following laparoscopic radical prostatectomy in Rajavithi Hospital, Thailand

Chang, J.Suk.; Park, Y.Hyun.; Ku, J.Hyun.; Kwak, C.; Kim, H.Hoe., 2012:
Predicting factors for death from other causes in patients with localized renal cell carcinoma

Sethasathien, S.; Charoenkwan, K.; Settakorn, J.; Srisomboon, J., 2014:
Predicting factors for positive vaginal surgical margin following radical hysterectomy for stage IB1 carcinoma of the cervix

Molina Escudero, R.; Alvarez Ardura, M.; Ripalda Ferretti, E.; Crespo Martínez, L.; González Avila, N.; Dorado Valentín, M.; Páez Borda, A., 2015:
Predicting factors for recurrence in low-grade Ta primary bladder tumours

Do, Y-Sun.; Kim, K-Won.; Chun, J-Kyong.; Cha, B.Ho.; Namgoong, M.Kyung.; Lee, H.Yong., 2010:
Predicting factors for refractory kawasaki disease

Phetcharat, M.; Putdivarnichapong, W.; Sitthimongkol, Y.; Apinuntavech, S., 2013:
Predicting factors for risk of depression in adolescents with learning disorders

Yu, S.Hyeon.; Ryu, J.Guk.; Jeong, S.Heon.; Hwang, E.Chang.; Jang, W.Seok.; Hwang, I.Sang.; Yu, H.Song.; Kim, S-Ouck.; Jung, S.Il.; Kang, T.Won.; Kwon, D.Deuk.; Park, K.; Hwang, J.Eul.; Kim, G.Soo., 2013:
Predicting factors for stent failure-free survival in patients with a malignant ureteral obstruction managed with ureteral stents

Kakimoto, K.; Sasaki, Y.; Kuroiwa, C.; Vong, S.; Kanal, K., 2010:
Predicting factors for the experience of HIV testing among women who have given birth in Cambodia

Geibprasert, S.; Krings, T.; Armstrong, D.; Terbrugge, K.G.; Raybaud, C.A., 2010:
Predicting factors for the follow-up outcome and management decisions in vein of Galen aneurysmal malformations

Okada, K-ichi.; Kawai, M.; Tani, M.; Hirono, S.; Miyazawa, M.; Shimizu, A.; Kitahata, Y.; Yamaue, H., 2015:
Predicting factors for unresectability in patients with pancreatic ductal adenocarcinoma

Kiertiburanakul, S.; Malathum, K.; Watcharananan, S.; Sathapatayavongs, B.; Sungkanuparph, S., 2009:
Predicting factors for unsuccessful switching from nevirapine to efavirenz in HIV-infected patients who developed nevirapine-associated skin rash

Kao, Y-Hsin.; Chen, C-Nan.; Chiang, J-Kun.; Chen, S-Shin.; Huang, W-Wei., 2009:
Predicting factors in the last week of survival in elderly patients with terminal cancer: a prospective study in southern Taiwan

Jang, H.Chan.; Park, Y.Jun.; Park, J.Shin., 2012:
Predicting factors of breakthrough infection in children with primary vesicoureteral reflux

Sae-Sia, W.; Songwathana, P.; Suwanmanee, M., 2016:
Predicting factors of care burden among caregivers of assault victims of the unrest in southern border provinces of Thailand

Wei, T.; Zeng, C.; Chen, L.; Wang, S.; Li, S.; Chen, Q.; Wang, L., 2004:
Predicting factors of depression in patients with primary hypertension: a community-based study

Tougeron, D.; Savoye, G.; Savoye-Collet, Céline.; Koning, E.; Michot, F.; Lerebours, E., 2008:
Predicting factors of fistula healing and clinical remission after infliximab-based combined therapy for perianal fistulizing Crohn's disease

Park, H.; Kim, N., 2008:
Predicting factors of physical activity in adolescents: a systematic review

Torigoe, K.; Sasaki, S.; Hoshina, J.; Torigoe, T.; Hojo, M.; Emura, S.; Kojima, K.; Onozuka, J.; Isobe, M.; Numata, O., 2012:
Predicting factors of plural hospitalization with pneumonia in low-birthweight infants

Tsai, H-Lin.; Yeh, Y-Sung.; Yu, F-Jung.; Lu, C-Yu.; Chen, C-Fan.; Chen, C-Wen.; Chang, Y-Tang.; Wang, J-Yuan., 2008:
Predicting factors of postoperative relapse in T2-4N0M0 colorectal cancer patients via harvesting a minimum of 12 lymph nodes

Lee, C.Hoon.; Shin, H.Phil.; Lee, J.Il.; Joo, K.Ro.; Cha, J.Myung.; Jeon, J.Won.; Lim, J.Uk.; Min, J.Ki.; Kim, D.Hee.; Kang, S.Wook.; Joung, H.Jun., 2014:
Predicting factors of present hepatitis C virus infection among patients positive for the hepatitis C virus antibody

Suttipong, C.; Sindhu, S., 2012:
Predicting factors of pressure ulcers in older Thai stroke patients living in urban communities

Hur, H.; Lee, H.Hong.; Jung, H.; Song, K.Young.; Jeon, H.Myung.; Park, C.Hyun., 2011:
Predicting factors of unexpected peritoneal seeding in locally advanced gastric cancer: indications for staging laparoscopy

Bahadori, F.; Ayatollahi, H.; Naghavi-Behzad, M.; Khalkhali, H.; Naseri, Z., 2014:
Predicting factors on cervical ripening and response to induction in women pregnant over 37 weeks

Han, S-Sook.; Lee, J-Im.; Kim, Y-Jung., 2008:
Predicting factors on eating behavior in coronary artery disease patients

Shimadera, S.; Iwai, N.; Deguchi, E.; Kimura, O.; Ono, S.; Furukawa, T.; Fumino, S., 2010:
Predicting factors on the occurrence of cystic dilatation of intrahepatic biliary system in biliary atresia

Ma, J-Chu.; Lee, P-Hsia.; Yang, Y-Cheng.; Chang, W-Yin., 2009:
Predicting factors related to nurses' intention to leave, job satisfaction, and perception of quality of care in acute care hospitals

Tongyoo, S.; Permpikul, C.; Haemin, R.; Epichath, N., 2013:
Predicting factors, incidence and prognosis of cardiac arrhythmia in medical, non-acute coronary syndrome, critically ill patients

Marcé, C.; Ezanno, P.; Seegers, H.; Pfeiffer, D.Udo.; Fourichon, C., 2014:
Predicting fadeout versus persistence of paratuberculosis in a dairy cattle herd for management and control purposes: a modelling study

Chang, A.; Boscardin, C.; Chou, C.L.; Loeser, H.; Hauer, K.E., 2010:
Predicting failing performance on a standardized patient clinical performance examination: the importance of communication and professionalism skills deficits

Krappinger, D.; Bizzotto, N.; Riedmann, S.; Kammerlander, C.; Hengg, C.; Kralinger, F.Sebastian., 2012:
Predicting failure after surgical fixation of proximal humerus fractures

Lawn, B.R.; Lee, J.Jin-Wu.; Constantino, P.J.; Lucas, P.W., 2009:
Predicting failure in mammalian enamel

Oldfield, M.; Dini, D.; Rodriguez y Baena, F., 2013:
Predicting failure in soft tissue phantoms via modeling of non-predetermined tear progression

Burroughs, A.K.; Triantos, C.K., 2007:
Predicting failure to control bleeding and mortality in acute variceal bleeding

Kuriyama, A.; Takahashi, Y.; Tsujimura, Y.; Miyazaki, K.; Satoh, T.; Ikeda, S.; Nakayama, T., 2016:
Predicting failure to follow-up screened high blood pressure in Japan: a cohort study

Nataf, G.F.; Castillo-Villa, P.O.; Sellappan, P.; Kriven, W.M.; Vives, E.; Planes, A.; Salje, E.K.H., 2015:
Predicting failure: acoustic emission of berlinite under compression

Estrin, I.; Goetz, R.; Hellerstein, D.J.; Bennett-Staub, A.; Seirmarco, G., 2010:
Predicting falls among psychiatric inpatients: a case-control study at a state psychiatric facility

Thevathasan, W.; Aziz, T., 2010:
Predicting falls in Parkinson disease: a step in the right direction

Overcash, J.A.; Beckstead, J., 2008:
Predicting falls in older patients using components of a comprehensive geriatric assessment

Morris, R., 2007:
Predicting falls in older women

Cameron, M.H.; Thielman, E.; Mazumder, R.; Bourdette, D., 2013:
Predicting falls in people with multiple sclerosis: fall history is as accurate as more complex measures

Nassar, N.; Helou, N.; Madi, C., 2015:
Predicting falls using two instruments (the Hendrich Fall Risk Model and the Morse Fall Scale) in an acute care setting in Lebanon

Josie, K.Leigh.; Peterson, C.Cant.; Burant, C.; Drotar, D.; Stancin, T.; Wade, S.L.; Yeates, K.; Taylor, H.Gerry., 2008:
Predicting family burden following childhood traumatic brain injury: a cumulative risk approach

Leathers, S.J.; Falconnier, L.; Spielfogel, J.E., 2010:
Predicting family reunification, adoption, and subsidized guardianship among adolescents in foster care

Anonymous, 1977:
Predicting famine

Davidson, L.E.; Wang, J.; Thornton, J.C.; Kaleem, Z.; Silva-Palacios, F.; Pierson, R.N.; Heymsfield, S.B.; Gallagher, D., 2011:
Predicting fat percent by skinfolds in racial groups: Durnin and Womersley revisited

Prieto, N.; Uttaro, B.; Mapiye, C.; Turner, T.D.; Dugan, M.E.R.; Zamora, V.; Young, M.; Beltranena, E., 2015:
Predicting fat quality from pigs fed reduced-oil corn dried distillers grains with solubles by near infrared reflectance spectroscopy: fatty acid composition and iodine value

Campbell, T.M.; Vallis, L.Ann., 2015:
Predicting fat-free mass index and sarcopenia in assisted-living older adults

McIntosh, E.I.; Smale, K.Brent.; Vallis, L.Ann., 2014:
Predicting fat-free mass index and sarcopenia: a pilot study in community-dwelling older adults

Passier, P.E.C.A.; Post, M.W.M.; van Zandvoort, M.J.E.; Rinkel, G.J.E.; Lindeman, E.; Visser-Meily, J.M.A., 2011:
Predicting fatigue 1 year after aneurysmal subarachnoid hemorrhage

Marion, M.Susan.; Wexler, A.S.; Hull, M.L., 2010:
Predicting fatigue during electrically stimulated non-isometric contractions

Huisman-de Waal, G.; Bazelmans, E.; van Achterberg, T.; Jansen, J.; Sauerwein, H.; Wanten, G.; Schoonhoven, L., 2011:
Predicting fatigue in patients using home parenteral nutrition: a longitudinal study

Carnelli, D.; Villa, T.; Gastaldi, D.; Pennati, G., 2012:
Predicting fatigue life of a PMMA based knee spacer using a multiaxial fatigue criterion

Creeeley, H.; Nesthus, T., 2007:
Predicting fatigue using voice analysis

van der Kwast, T.H.; Bapat, B., 2009:
Predicting favourable prognosis of urothelial carcinoma: gene expression and genome profiling

Ziner, K.Wagler.; Sledge, G.W.; Bell, C.J.; Johns, S.; Miller, K.D.; Champion, V.L., 2012:
Predicting fear of breast cancer recurrence and self-efficacy in survivors by age at diagnosis

Brown, J.; Crison, J.; Timmins, P., 2012:
Predicting feasibility and characterizing performance of extended-release formulations using physiologically based pharmacokinetic modeling

Paesmans, M.; Klastersky, J.; Maertens, J.; Georgala, A.; Muanza, Fédérique.; Aoun, M.; Ferrant, A.; Rapoport, B.; Rolston, K.; Ameye, L., 2011:
Predicting febrile neutropenic patients at low risk using the MASCC score: does bacteremia matter?

Schale, S.P.; Le, T.M.; Pierce, K.M., 2012:
Predicting feedstock and percent composition for blends of biodiesel with conventional diesel using chemometrics and gas chromatography-mass spectrometry

DiBiase, R.; Miller, P.M., 2012:
Predicting feelings of cognitive competence in head start preschoolers

Patton, G.C.; Olsson, C.; Bond, L.; Toumbourou, J.W.; Carlin, J.B.; Hemphill, S.A.; Catalano, R.F., 2008:
Predicting female depression across puberty: a two-nation longitudinal study

Polishchuk, D.L.; Patrick, D.A.; Gvozdyev, B.V.; Lee, J.H.; Geller, J.A.; Macaulay, W., 2014:
Predicting femoral head diameter and lesser trochanter to center of femoral head distance: a novel method of templating hip hemiarthroplasty

Maheshwari, A.; Bhattacharya, S.; Johnson, N.P., 2008:
Predicting fertility

Anderson, K.S.; Segars, J.H., 2013:
Predicting fertility with antimüllerian hormone: is a cutoff value adequate?

Oztekin, D.; Aydal, I.; Oztekin, O.; Okcu, S.; Borekci, R.; Tinar, S., 2010:
Predicting fetal asphyxia in intrahepatic cholestasis of pregnancy

Papastefanou, I.; Souka, A.P.; Eleftheriades, M.; Pilalis, A.; Chrelias, C.; Kassanos, D., 2016:
Predicting fetal growth deviation in parous women: combining the birth weight of the previous pregnancy and third trimester ultrasound scan

Zork, N.M.; Pierce, S.; Zollinger, T.; Kominiarek, M., 2015:
Predicting fetal karyotype in fetuses with omphalocele: The current role of ultrasound

Schenone, M.H.; Samson, J.E.; Jenkins, L.; Suhag, A.; Mari, G., 2015:
Predicting fetal lung maturity using the fetal pulmonary artery Doppler wave acceleration/ejection time ratio

Ablin, J.N.; Buskila, D., 2015:
Predicting fibromyalgia, a narrative review: are we better than fools and children?

Arslan, H.; Tasan, M.; Yildirim, D.; Koksal, Eüp.Selim.; Cemek, B., 2014:
Predicting field capacity, wilting point, and the other physical properties of soils using hyperspectral reflectance spectroscopy: two different statistical approaches

Olney, A.McGregor., 2013:
Predicting film genres with implicit ideals

Bagher-Ebadian, H.; Jafari-Khouzani, K.; Mitsias, P.D.; Lu, M.; Soltanian-Zadeh, H.; Chopp, M.; Ewing, J.R., 2011:
Predicting final extent of ischemic infarction using artificial neural network analysis of multi-parametric MRI in patients with stroke

Vigh, Tás.; Drávavölgyi, Gábor.; Sóti, Péter.L.; Pataki, H.; Igricz, Tás.; Wagner, Ián.; Vajna, Bázs.; Madarász, János.; Marosi, Görgy.; Nagy, Z.K., 2015:
Predicting final product properties of melt extruded solid dispersions from process parameters using Raman spectrometry

Langabeer, J., 2009:
Predicting financial distress in teaching hospitals

Sun, Y.; Hollerbach, J.M.; Mascaro, S.A., 2008:
Predicting fingertip forces by imaging coloration changes in the fingernail and surrounding skin

Lawrie, S.M.; Stanfield, A.; Johnstone, E.C.; McIntosh, A.M., 2013:
Predicting first episode psychosis in those at high risk for genetic or cognitive reasons

Godin, G.; Germain, M., 2014:
Predicting first lifetime plasma donation among whole blood donors

Lee, S-Pyo.; Delong, R.; Hodges, J.S.; Hayashi, K.; Lee, J-Bong., 2007:
Predicting first molar width using virtual models of dental arches

Hornung, C.; Schiltz, C.; Brunner, M.; Martin, R., 2014:
Predicting first-grade mathematics achievement: the contributions of domain-general cognitive abilities, nonverbal number sense, and early number competence

Chua, H-Ruey.; Lau, T.; Luo, N.; Ma, V.; Teo, B-Wee.; Haroon, S.; Choy, K-Loong.; Lim, Y-Ching.; Chng, W-Qiang.; Ong, L-Zhen.; Wong, T-Yeung.; Lee, E.J., 2015:
Predicting first-year mortality in incident dialysis patients with end-stage renal disease - the UREA5 study

Tanneberger, K.; Knöbel, M.; Busser, F.J.M.; Sinnige, T.L.; Hermens, J.L.M.; Schirmer, K., 2013:
Predicting fish acute toxicity using a fish gill cell line-based toxicity assay

Budy, P.; Baker, M.; Dahle, S.K., 2012:
Predicting fish growth potential and identifying water quality constraints: a spatially-explicit bioenergetics approach

Kay, L.G.; Bundy, A.C.; Clemson, L.M., 2009:
Predicting fitness to drive in people with cognitive impairments by using DriveSafe and DriveAware

Kay, L.G.; Bundy, A.C.; Clemson, L.M., 2008:
Predicting fitness to drive using the visual recognition slide test (USyd)

Caudarella, R.; Tonello, L.; Rizzoli, E.; Vescini, F., 2011:
Predicting five-year recurrence rates of kidney stones: an artificial neural network model

El-Manzalawy, Y.; Dobbs, D.; Honavar, V., 2009:
Predicting flexible length linear B-cell epitopes

Danielson, M.L.; Lill, M.A., 2012:
Predicting flexible loop regions that interact with ligands: the challenge of accurate scoring

Nielsen, K.; Cleal, B., 2010:
Predicting flow at work: investigating the activities and job characteristics that predict flow states at work

Cheng, Z.; Juli, C.; Wood, N.B.; Gibbs, R.G.J.; Xu, X.Y., 2015:
Predicting flow in aortic dissection: comparison of computational model with PC-MRI velocity measurements

Masicampo, E.J.; Ambady, N., 2014:
Predicting fluctuations in widespread interest: memory decay and goal-related memory accessibility in internet search trends

Biais, M.; Calderon, J.; Pernot, M.; Barandon, L.; Couffinhal, T.; Ouattara, A.; Sztark, Fçois., 2014:
Predicting fluid responsiveness during infrarenal aortic cross-clamping in pigs

Gan, H.; Cannesson, M.; Chandler, J.R.; Ansermino, J.Mark., 2014:
Predicting fluid responsiveness in children: a systematic review

Stricker, P.A.; Fiadjoe, J.E.; Jobes, D.R., 2011:
Predicting fluid responsiveness in children: are the studied indicators of value in the setting of loss and replacement?

Pereira de Souza Neto, E.; Grousson, S.; Duflo, F.; Ducreux, C.; Joly, H.; Convert, J.; Mottolese, C.; Dailler, F.; Cannesson, M., 2011:
Predicting fluid responsiveness in mechanically ventilated children under general anaesthesia using dynamic parameters and transthoracic echocardiography

Lekerika, N.; Gutiérrez Rico, R.M.; Arco Vázquez, J.; Prieto Molano, L.; Arana-Arri, E.; Martínez Indart, L.; Martínez Ruiz, A.; Ortiz de Urbina López, J., 2015:
Predicting fluid responsiveness in patients undergoing orthotopic liver transplantation: effects on intraoperative blood transfusion and postoperative complications

Wacharasint, P.; Lertamornpong, A.; Wattanathum, A.; Wongsa, A., 2012:
Predicting fluid responsiveness in septic shock patients by using 3 dynamic indices: is it all equally effective?

Runcie, C., 2007:
Predicting fluid responsiveness in theatre

Cannesson, M.; Tran, N.Phuong.; Cho, M.; Hatib, F.; Michard, F., 2012:
Predicting fluid responsiveness with stroke volume variation despite multiple extrasystoles

Callis, P.R., 2011 :
Predicting fluorescence lifetimes and spectra of biopolymers

Ezzeldin, H.H.; Diasio, R.B., 2008:
Predicting fluorouracil toxicity: can we finally do it?

Zhang, Z.; Wang, L.; Gao, Y.; Zhang, J.; Zhenirovskyy, M.; Alexov, E., 2012:
Predicting folding free energy changes upon single point mutations

Li, Y.; Zhang, S., 2013:
Predicting folding pathways between RNA conformational structures guided by RNA stacks

Oliver, J.M.; Lambert, B.S.; Martin, S.E.; Green, J.S.; Crouse, S.F., 2013:
Predicting football players' dual-energy x-ray absorptiometry body composition using standard anthropometric measures

Bartsch, R.; D.V.ies, C.; Pluschnig, U.; Dubsky, P.; Bago-Horvath, Z.; Gampenrieder, S.P.; Rudas, M.; Mader, R.M.; Rottenfusser, A.; Wiltschke, C.; Gnant, M.; Zielinski, C.C.; Steger, G.G., 2010:
Predicting for activity of second-line trastuzumab-based therapy in her2-positive advanced breast cancer

Alden, C.J., 2010:
Predicting for human pharmaceutical cancer risk

Yoost, T.; Rames, R.; Lebed, B.; Bhavsar, R.; Rovner, E., 2011:
Predicting for postoperative incontinence following sling incision

Duvail, M.; Arleth, L.; Zemb, T.; Dufrêche, J-François., 2014:
Predicting for thermodynamic instabilities in water/oil/surfactant microemulsions: a mesoscopic modelling approach

Gonzalez-Izal, M.; Falla, D.; Izquierdo, M.; Farina, D., 2010:
Predicting force loss during dynamic fatiguing exercises from non-linear mapping of features of the surface electromyogram

Zendejas, B.; Hoskin, T.L.; Degnim, A.C.; Reynolds, C.A.; Farley, D.R.; Boughey, J.C., 2011:
Predicting four or more metastatic axillary lymph nodes in patients with sentinel node-positive breast cancer: assessment of existent risk scores