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.
Highlights
IntroductionPublisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations
We summarized the reviewed models to generate a workflow for developing a predictive model that is able to solve human disease-related tasks using epigenomic data in libraries, and evaluation metrics
Existing Deep learning (DL)-based predictive models for human disease-related tasks including disease detection, subtype classification, and treatment response prediction primarily dealt with classification tasks whose outcome is basically a discrete variable [21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42]
Summary
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. Existing evidence highlighted an important role of epigenetic biomarkers in a wide range of human diseases in terms of early detection, subtype classification, prognosis, and predicting response to therapy [8,9,10] For this reason, translational epigenomics that seeks to leverage associations between epigenomic marks and clinical outcomes has received great concern in recent years [11]. Zhang et al [3] and Min et al [4] provided a useful guideline which allows researchers from various backgrounds to understand and utilize DL to solve omics-related problems, whereas Talukder et al [12] attempted to unbox the black-box nature of DL, increasing the interpretability of DL in epigenomics These works focused on biological mechanisms and model structures rather than clinical outcomes of human diseases. Proposing current practical challenges and future trends of the development of epigenomic data-based DL techniques for translational medicine
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