Investigating Core Signaling Pathways of Hepatitis B Virus Pathogenesis for Biomarkers Identification and Drug Discovery via Systems Biology and Deep Learning Method

Abstract

Hepatitis B Virus (HBV) infection is a major cause of morbidity and mortality worldwide. However, poor understanding of its pathogenesis often gives rise to intractable immune escape and prognosis recurrence. Thus, a valid systematic approach based on big data mining and genome-wide RNA-seq data is imperative to further investigate the pathogenetic mechanism and identify biomarkers for drug design. In this study, systems biology method was applied to trim false positives from the host/pathogen genetic and epigenetic interaction network (HPI-GEN) under HBV infection by two-side RNA-seq data. Then, via the principal network projection (PNP) approach and the annotation of KEGG (Kyoto Encyclopedia of Genes and Genomes) pathways, significant biomarkers related to cellular dysfunctions were identified from the core cross-talk signaling pathways as drug targets. Further, based on the pre-trained deep learning-based drug-target interaction (DTI) model and the validated pharmacological properties from databases, i.e., drug regulation ability, toxicity, and sensitivity, a combination of promising multi-target drugs was designed as a multiple-molecule drug to create more possibility for the treatment of HBV infection. Therefore, with the proposed systems medicine discovery and repositioning procedure, we not only shed light on the etiologic mechanism during HBV infection but also efficiently provided a potential drug combination for therapeutic treatment of Hepatitis B.

Deep Learning for Human Disease Detection, Subtype Classification, and Treatment Response Prediction Using Epigenomic Data

“…Applications of DL to assist physicians and scientists in clinical settings using epigenomic data have been relatively unexplored until very recently. Except for one paper published in 2016, 21 out of 22 papers reviewed were published in the last 5 years and a majority of the models were developed by USA and China research teams [ 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 ]. This suggests a novel field of study that gains an increasing interest.…”

“…Among human disease-related tasks, disease detection, subtype classification, and treatment response prediction received great concerns. Existing evidence indicated that DL models in epigenomics for solving the human disease-related tasks outperformed [ 23 , 24 , 29 , 41 ] or at least competitive to traditional ML models [ 37 ]. Some predictive models in previous studies utilized DL as a powerful component of a multi-step process [ 28 , 29 , 30 , 31 , 38 , 39 ].…”

“…Various network architectures were employed such as multi-layer perceptron, autoencoder and its variants, convolutional neural network, and deep belief network. Reviewed models yielded high accuracy ranged from 88.3% to 100.0% for disease detection tasks [ 23 , 24 , 28 , 29 , 30 , 31 ], from 69.5% to 97.8% for subtype classification tasks [ 32 , 33 , 35 , 36 , 37 , 38 , 40 ], and from 80.0% to 93.0% for treatment response prediction tasks [ 41 , 42 ].…”

“…Chang et al [ 41 ] investigated the pathogenic mechanism and biomarkers for hepatitis B virus drug development using a systematic approach based on big data mining and genome-wide RNA-seq data. As a part of this approach, a fully-connected neural network was employed to predict drug–target interactions.…”

“…There are heaps of loss functions for regression and classification tasks. As all existing DL models for translational epigenomics dealt with the latter [ 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 ], this section covers the most commonly used binary and multi-class classification loss functions.…”