Predicting environmentally responsive transgenerational differential DNA methylated regions (epimutations) in the genome using a hybrid deep-machine learning approach

Abstract Background Deep learning is an active bioinformatics artificial intelligence field that is useful in solving many biological problems, including predicting altered epigenetics such as DNA methylation regions. Deep learning (DL) can learn an informative representation that addresses the need...

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Autores principales:	Pegah Mavaie, Lawrence Holder, Daniel Beck, Michael K. Skinner
Formato:	article
Lenguaje:	EN
Publicado:	BMC 2021
Materias:	Deep learning Machine learning Artificial intelligence DNA methylation Epigenetics Transgenerational Computer applications to medicine. Medical informatics R858-859.7 Biology (General) QH301-705.5
Acceso en línea:	https://doaj.org/article/11d9440dcac14dd09160739ebfdd289e
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spelling	oai:doaj.org-article:11d9440dcac14dd09160739ebfdd289e2021-12-05T12:08:41ZPredicting environmentally responsive transgenerational differential DNA methylated regions (epimutations) in the genome using a hybrid deep-machine learning approach10.1186/s12859-021-04491-z1471-2105https://doaj.org/article/11d9440dcac14dd09160739ebfdd289e2021-11-01T00:00:00Zhttps://doi.org/10.1186/s12859-021-04491-zhttps://doaj.org/toc/1471-2105Abstract Background Deep learning is an active bioinformatics artificial intelligence field that is useful in solving many biological problems, including predicting altered epigenetics such as DNA methylation regions. Deep learning (DL) can learn an informative representation that addresses the need for defining relevant features. However, deep learning models are computationally expensive, and they require large training datasets to achieve good classification performance. Results One approach to addressing these challenges is to use a less complex deep learning network for feature selection and Machine Learning (ML) for classification. In the current study, we introduce a hybrid DL-ML approach that uses a deep neural network for extracting molecular features and a non-DL classifier to predict environmentally responsive transgenerational differential DNA methylated regions (DMRs), termed epimutations, based on the extracted DL-based features. Various environmental toxicant induced epigenetic transgenerational inheritance sperm epimutations were used to train the model on the rat genome DNA sequence and use the model to predict transgenerational DMRs (epimutations) across the entire genome. Conclusion The approach was also used to predict potential DMRs in the human genome. Experimental results show that the hybrid DL-ML approach outperforms deep learning and traditional machine learning methods.Pegah MavaieLawrence HolderDaniel BeckMichael K. SkinnerBMCarticleDeep learningMachine learningArtificial intelligenceDNA methylationEpigeneticsTransgenerationalComputer applications to medicine. Medical informaticsR858-859.7Biology (General)QH301-705.5ENBMC Bioinformatics, Vol 22, Iss 1, Pp 1-25 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Deep learning Machine learning Artificial intelligence DNA methylation Epigenetics Transgenerational Computer applications to medicine. Medical informatics R858-859.7 Biology (General) QH301-705.5
spellingShingle	Deep learning Machine learning Artificial intelligence DNA methylation Epigenetics Transgenerational Computer applications to medicine. Medical informatics R858-859.7 Biology (General) QH301-705.5 Pegah Mavaie Lawrence Holder Daniel Beck Michael K. Skinner Predicting environmentally responsive transgenerational differential DNA methylated regions (epimutations) in the genome using a hybrid deep-machine learning approach
description	Abstract Background Deep learning is an active bioinformatics artificial intelligence field that is useful in solving many biological problems, including predicting altered epigenetics such as DNA methylation regions. Deep learning (DL) can learn an informative representation that addresses the need for defining relevant features. However, deep learning models are computationally expensive, and they require large training datasets to achieve good classification performance. Results One approach to addressing these challenges is to use a less complex deep learning network for feature selection and Machine Learning (ML) for classification. In the current study, we introduce a hybrid DL-ML approach that uses a deep neural network for extracting molecular features and a non-DL classifier to predict environmentally responsive transgenerational differential DNA methylated regions (DMRs), termed epimutations, based on the extracted DL-based features. Various environmental toxicant induced epigenetic transgenerational inheritance sperm epimutations were used to train the model on the rat genome DNA sequence and use the model to predict transgenerational DMRs (epimutations) across the entire genome. Conclusion The approach was also used to predict potential DMRs in the human genome. Experimental results show that the hybrid DL-ML approach outperforms deep learning and traditional machine learning methods.
format	article
author	Pegah Mavaie Lawrence Holder Daniel Beck Michael K. Skinner
author_facet	Pegah Mavaie Lawrence Holder Daniel Beck Michael K. Skinner
author_sort	Pegah Mavaie
title	Predicting environmentally responsive transgenerational differential DNA methylated regions (epimutations) in the genome using a hybrid deep-machine learning approach
title_short	Predicting environmentally responsive transgenerational differential DNA methylated regions (epimutations) in the genome using a hybrid deep-machine learning approach
title_full	Predicting environmentally responsive transgenerational differential DNA methylated regions (epimutations) in the genome using a hybrid deep-machine learning approach
title_fullStr	Predicting environmentally responsive transgenerational differential DNA methylated regions (epimutations) in the genome using a hybrid deep-machine learning approach
title_full_unstemmed	Predicting environmentally responsive transgenerational differential DNA methylated regions (epimutations) in the genome using a hybrid deep-machine learning approach
title_sort	predicting environmentally responsive transgenerational differential dna methylated regions (epimutations) in the genome using a hybrid deep-machine learning approach
publisher	BMC
publishDate	2021
url	https://doaj.org/article/11d9440dcac14dd09160739ebfdd289e
work_keys_str_mv	AT pegahmavaie predictingenvironmentallyresponsivetransgenerationaldifferentialdnamethylatedregionsepimutationsinthegenomeusingahybriddeepmachinelearningapproach AT lawrenceholder predictingenvironmentallyresponsivetransgenerationaldifferentialdnamethylatedregionsepimutationsinthegenomeusingahybriddeepmachinelearningapproach AT danielbeck predictingenvironmentallyresponsivetransgenerationaldifferentialdnamethylatedregionsepimutationsinthegenomeusingahybriddeepmachinelearningapproach AT michaelkskinner predictingenvironmentallyresponsivetransgenerationaldifferentialdnamethylatedregionsepimutationsinthegenomeusingahybriddeepmachinelearningapproach
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Predicting environmentally responsive transgenerational differential DNA methylated regions (epimutations) in the genome using a hybrid deep-machine learning approach

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