DRREP: deep ridge regressed epitope predictor.

Journal: BMC genomics
Published Date:

Abstract

INTRODUCTION: The 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).

Authors

  • Gene Sher
    Department of Computer Science, University of Central Florida, Orlando, FL, USA. gsher@knights.ucf.edu.
  • Degui Zhi
    School of Biomedical Informatics, the University of Texas Health Science Center at Houston, Houston, TX, USA.
  • Shaojie Zhang
    Department of Computer Science, University of Central Florida, Orlando, 32816-2362 Florida USA.