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

Prediction of RNA-protein interactions by combining deep convolutional neural network with feature selection ensemble method



Prediction of RNA-protein interactions by combining deep convolutional neural network with feature selection ensemble method



Journal of Theoretical Biology 461: 230-238



RNA-protein interaction (RPI) plays an important role in the basic cellular processes of organisms. Unfortunately, due to time and cost constraints, it is difficult for biological experiments to determine the relationship between RNA and protein to a large extent. So there is an urgent need for reliable computational methods to quickly and accurately predict RNA-protein interaction. In this study, we propose a novel computational method RPIFSE (predicting RPI with Feature Selection Ensemble method) based on RNA and protein sequence information to predict RPI. Firstly, RPIFSE disturbs the features extracted by the convolution neural network (CNN) and generates multiple data sets according to the weight of the feature, and then use extreme learning machine (ELM) classifier to classify these data sets. Finally, the results of each classifier are combined, and the highest score is chosen as the final prediction result by weighting voting method. In 5-fold cross-validation experiments, RPIFSE achieved 91.87%, 89.74%, 97.76% and 98.98% accuracy on RPI369, RPI2241, RPI488 and RPI1807 data sets, respectively. To further evaluate the performance of RPIFSE, we compare it with the state-of-the-art support vector machine (SVM) classifier and other exiting methods on those data sets. Furthermore, we also predicted the RPI on the independent data set NPInter2.0 and drew the network graph based on the prediction results. These promising comparison results demonstrated the effectiveness of RPIFSE and indicated that RPIFSE could be a useful tool for predicting RPI.

Please choose payment method:






(PDF emailed within 0-6 h: $19.90)

Accession: 065885141

Download citation: RISBibTeXText

PMID: 30321541

DOI: 10.1016/j.jtbi.2018.10.029


Related references

Combining High Speed ELM Learning with a Deep Convolutional Neural Network Feature Encoding for Predicting Protein-RNA Interactions. Ieee/Acm Transactions on Computational Biology and Bioinformatics 2018:, 2018

Bearing Fault Diagnosis with a Feature Fusion Method Based on an Ensemble Convolutional Neural Network and Deep Neural Network. Sensors 19(9):, 2019

Dose distribution prediction in isodose feature-preserving voxelization domain using deep convolutional neural network. Medical Physics 46(7): 2978-2987, 2019

A generic deep convolutional neural network framework for prediction of receptor-ligand interactions-NetPhosPan: application to kinase phosphorylation prediction. Bioinformatics 35(7): 1098-1107, 2019

Bearing Fault Diagnosis Method Based on Deep Convolutional Neural Network and Random Forest Ensemble Learning. Sensors 19(5):, 2019

A combinational feature selection and ensemble neural network method for classification of gene expression data. Bmc Bioinformatics 5: 136-136, 2004

An Ensemble Deep Convolutional Neural Network Model with Improved D-S Evidence Fusion for Bearing Fault Diagnosis. Sensors 17(8):, 2017

Deep feature learning for knee cartilage segmentation using a triplanar convolutional neural network. Medical Image Computing and Computer-Assisted Intervention 16(Pt 2): 246-253, 2013

Automated selection of myocardial inversion time with a convolutional neural network: Spatial temporal ensemble myocardium inversion network (STEMI-NET). Magnetic Resonance in Medicine 81(5): 3283-3291, 2019

Learning Traffic as Images: A Deep Convolutional Neural Network for Large-Scale Transportation Network Speed Prediction. Sensors 17(4):, 2017

Lung cancer risk prediction method based on feature selection and artificial neural network. Asian Pacific Journal of Cancer Prevention 15(23): 10539-10542, 2014

Design of deep convolutional neural network architectures for automated feature extraction in industrial inspection. Cirp Annals - Manufacturing Technology, 2016

Chromatin accessibility prediction via a hybrid deep convolutional neural network. Bioinformatics 34(5): 732-738, 2018

Prediction of CRISPR sgRNA Activity Using a Deep Convolutional Neural Network. Journal of Chemical Information and Modeling 59(1): 615-624, 2019

Road Surface Classification Using a Deep Ensemble Network with Sensor Feature Selection. Sensors 18(12):, 2018