ConvChrome: Predicting Gene Expression Based on Histone Modifications using Deep Learning Techniques.

Abstract

Background: Gene regulation is a complex and a dynamic process that not only depends on the DNA sequence of genes, but also is influenced by a key factor called Epigenetic Mechanisms. This factor along with other factors contributes to change the behavior of DNA. While these factors cannot affect the structure of DNA, they can control the behavior of DNA by turning genes "on" or "off" that leads to determine which proteins are transcribed. Objective: This paper will focus on histone modifications mechanism, histones are the group of proteins that bundle the DNA into a structural form called nucleosomes (coils); how DNA wraps with these histone proteins describes how gene can be accessed to express or not. When histones bound tightly to DNA, that make the gene cannot be expressed and vise verse. It is important to know Histone Modifications’ combinatorial patterns, and how these combinatorial patterns can affect and work together to control the process of gene expression. Methods: In this paper, ConvChrome deep learning methodologies are proposed for predicting the gene expression behavior from Histone modifications data as an input to use more than one Convolutional Network model, this happens in order to recognize patterns of histones signals and to interpret their spatial relationship arranged on chromatin structure to give insights into regulatory signatures of histone modifications. Results and Conclusion: The experiments results show that ConvChrome achieved 88.741 % in terms of Area under the Curve (AUC) score, which is an outstanding improvement over the baseline for gene expression classification prediction task from combinatorial interactions among five histone modifications on 56 different cell-types.