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

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 t...

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Autores principales:	Thi Mai Nguyen, Nackhyoung Kim, Da Hae Kim, Hoang Long Le, Md Jalil Piran, Soo-Jong Um, Jin Hee Kim
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	deep learning epigenomics disease detection subtype classification treatment response prediction systematic review Biology (General) QH301-705.5
Acceso en línea:	https://doaj.org/article/0588bf3f171b436b89708059383f6da1
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spelling	oai:doaj.org-article:0588bf3f171b436b89708059383f6da12021-11-25T16:51:33ZDeep Learning for Human Disease Detection, Subtype Classification, and Treatment Response Prediction Using Epigenomic Data10.3390/biomedicines91117332227-9059https://doaj.org/article/0588bf3f171b436b89708059383f6da12021-11-01T00:00:00Zhttps://www.mdpi.com/2227-9059/9/11/1733https://doaj.org/toc/2227-9059Deep 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.Thi Mai NguyenNackhyoung KimDa Hae KimHoang Long LeMd Jalil PiranSoo-Jong UmJin Hee KimMDPI AGarticledeep learningepigenomicsdisease detectionsubtype classificationtreatment response predictionsystematic reviewBiology (General)QH301-705.5ENBiomedicines, Vol 9, Iss 1733, p 1733 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	deep learning epigenomics disease detection subtype classification treatment response prediction systematic review Biology (General) QH301-705.5
spellingShingle	deep learning epigenomics disease detection subtype classification treatment response prediction systematic review Biology (General) QH301-705.5 Thi Mai Nguyen Nackhyoung Kim Da Hae Kim Hoang Long Le Md Jalil Piran Soo-Jong Um Jin Hee Kim Deep Learning for Human Disease Detection, Subtype Classification, and Treatment Response Prediction Using Epigenomic Data
description	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.
format	article
author	Thi Mai Nguyen Nackhyoung Kim Da Hae Kim Hoang Long Le Md Jalil Piran Soo-Jong Um Jin Hee Kim
author_facet	Thi Mai Nguyen Nackhyoung Kim Da Hae Kim Hoang Long Le Md Jalil Piran Soo-Jong Um Jin Hee Kim
author_sort	Thi Mai Nguyen
title	Deep Learning for Human Disease Detection, Subtype Classification, and Treatment Response Prediction Using Epigenomic Data
title_short	Deep Learning for Human Disease Detection, Subtype Classification, and Treatment Response Prediction Using Epigenomic Data
title_full	Deep Learning for Human Disease Detection, Subtype Classification, and Treatment Response Prediction Using Epigenomic Data
title_fullStr	Deep Learning for Human Disease Detection, Subtype Classification, and Treatment Response Prediction Using Epigenomic Data
title_full_unstemmed	Deep Learning for Human Disease Detection, Subtype Classification, and Treatment Response Prediction Using Epigenomic Data
title_sort	deep learning for human disease detection, subtype classification, and treatment response prediction using epigenomic data
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/0588bf3f171b436b89708059383f6da1
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Deep Learning for Human Disease Detection, Subtype Classification, and Treatment Response Prediction Using Epigenomic Data

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