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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: Nguyen, Thi Mai, Kim, Nackhyoung, Kim, Da Hae, Le, Hoang Long, Piran, Md Jalil, Um, Soo-Jong, Kim, Jin Hee
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8615388/
https://www.ncbi.nlm.nih.gov/pubmed/34829962
http://dx.doi.org/10.3390/biomedicines9111733
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author Nguyen, Thi Mai
Kim, Nackhyoung
Kim, Da Hae
Le, Hoang Long
Piran, Md Jalil
Um, Soo-Jong
Kim, Jin Hee
author_facet Nguyen, Thi Mai
Kim, Nackhyoung
Kim, Da Hae
Le, Hoang Long
Piran, Md Jalil
Um, Soo-Jong
Kim, Jin Hee
author_sort Nguyen, Thi Mai
collection PubMed
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.
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spelling pubmed-86153882021-11-26 Deep Learning for Human Disease Detection, Subtype Classification, and Treatment Response Prediction Using Epigenomic Data Nguyen, Thi Mai Kim, Nackhyoung Kim, Da Hae Le, Hoang Long Piran, Md Jalil Um, Soo-Jong Kim, Jin Hee Biomedicines Review 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. MDPI 2021-11-20 /pmc/articles/PMC8615388/ /pubmed/34829962 http://dx.doi.org/10.3390/biomedicines9111733 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Review
Nguyen, Thi Mai
Kim, Nackhyoung
Kim, Da Hae
Le, Hoang Long
Piran, Md Jalil
Um, Soo-Jong
Kim, Jin Hee
Deep Learning for Human Disease Detection, Subtype Classification, and Treatment Response Prediction Using Epigenomic Data
title 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_short 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
topic Review
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8615388/
https://www.ncbi.nlm.nih.gov/pubmed/34829962
http://dx.doi.org/10.3390/biomedicines9111733
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