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Journal article
Predicting environmentally responsive transgenerational differential DNA methylated regions (epimutations) in the genome using a hybrid deep-machine learning approach
BMC bioinformatics, Vol.22(1), pp.1-575
11/30/2021
PMCID: PMC8630850
PMID: 34847877
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.
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Details
- Title
- Predicting environmentally responsive transgenerational differential DNA methylated regions (epimutations) in the genome using a hybrid deep-machine learning approach
- Creators
- Pegah Mavaie - Washington State UniversityLawrence Holder - Washington State UniversityDaniel Beck - Washington State UniversityMichael K. Skinner - Washington State University
- Publication Details
- BMC bioinformatics, Vol.22(1), pp.1-575
- Academic Unit
- Biological Sciences, School of
- Publisher
- Springer Nature
- Number of pages
- 25
- Grant note
- 50183; 61174 / John Templeton Foundation ES012974 / NIH; United States Department of Health & Human Services; National Institutes of Health (NIH) - USA
- Identifiers
- 99901080818801842
- Language
- English
- Resource Type
- Journal article