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

Abstract

Deep learning (DL) is a distinct class of machine learning that has achieved first-class performance in many fields of study. For epigenomics, the application of DL to assist physicians and scientists in human disease-relevant prediction tasks has been relatively unexplored until very recently. In this article, we critically review published studies that employed DL models to predict disease detection, subtype classification, and treatment responses, using epigenomic data. A comprehensive search on PubMed, Scopus, Web of Science, Google Scholar, and arXiv.org was performed following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. Among 1140 initially identified publications, we included 22 articles in our review. DNA methylation and RNA-sequencing data are most frequently used to train the predictive models. The reviewed models achieved a high accuracy ranged from 88.3% to 100.0% for disease detection tasks, from 69.5% to 97.8% for subtype classification tasks, and from 80.0% to 93.0% for treatment response prediction tasks. We generated a workflow to develop a predictive model that encompasses all steps from first defining human disease-related tasks to finally evaluating model performance. DL holds promise for transforming epigenomic big data into valuable knowledge that will enhance the development of translational epigenomics.

Keywords: deep learning; disease detection; epigenomics; subtype classification; systematic review; treatment response prediction.

Figure 1

PRISMA flow for study selection.

Figure 2

A workflow for developing a predictive model in translational epigenomics.

Figure 3

Data preprocessing flow for developing a predictive model in epigenomics.

Figure 4

DL architectures that have been applied in epigenomics to solve some human diseases-related prediction tasks. (A) Multi-layer perceptron, (B) Autoencoder, (C) Variational autoencoder, (D) Convolutional neural network, (E) Deep belief network.