DRREP: deep ridge regressed epitope predictor

BMC Genomics

Research

Shaojie Zhang¹  Gene Sher¹  Degui Zhi² 

[1] Department of Computer Science, University of Central Florida, Orlando, FL, USA;School of Biomedical Informatics, University of Texas Health Science Center at Houston, Houston, TX, USA;

关键词: Epitope prediction;    Deep network;    Neural network;    Analytical learning;    Linear epitope;    Continuous epitope;    Convolutional network;    String kernel;   

DOI  :  10.1186/s12864-017-4024-8

来源: Springer

【 摘 要 】

IntroductionThe ability to predict epitopes plays an enormous role in vaccine development in terms of our ability to zero in on where to do a more thorough in-vivo analysis of the protein in question. Though for the past decade there have been numerous advancements and improvements in epitope prediction, on average the best benchmark prediction accuracies are still only around 60%. New machine learning algorithms have arisen within the domain of deep learning, text mining, and convolutional networks. This paper presents a novel analytically trained and string kernel using deep neural network, which is tailored for continuous epitope prediction, called: Deep Ridge Regressed Epitope Predictor (DRREP).ResultsDRREP was tested on long protein sequences from the following datasets: SARS, Pellequer, HIV, AntiJen, and SEQ194. DRREP was compared to numerous state of the art epitope predictors, including the most recently published predictors called LBtope and DMNLBE. Using area under ROC curve (AUC), DRREP achieved a performance improvement over the best performing predictors on SARS (13.7%), HIV (8.9%), Pellequer (1.5%), and SEQ194 (3.1%), with its performance being matched only on the AntiJen dataset, by the LBtope predictor, where both DRREP and LBtope achieved an AUC of 0.702.ConclusionDRREP is an analytically trained deep neural network, thus capable of learning in a single step through regression. By combining the features of deep learning, string kernels, and convolutional networks, the system is able to perform residue-by-residue prediction of continues epitopes with higher accuracy than the current state of the art predictors.

【 授权许可】

CC BY   
© The Author(s) 2017