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CC BY-NC-ND V4.0, Open Access
Journal article
Machine learning for epigenetics and future medical applications
Epigenetics, Vol.12(7), pp.505-514
07/03/2017
Handle:
https://hdl.handle.net/2376/106514
PMCID: PMC5687335
PMID: 28524769
Abstract
Understanding epigenetic processes holds immense promise for medical applications. Advances in Machine Learning (ML) are critical to realize this promise. Previous studies used epigenetic data sets associated with the germline transmission of epigenetic transgenerational inheritance of disease and novel ML approaches to predict genome-wide locations of critical epimutations. A combination of Active Learning (ACL) and Imbalanced Class Learning (ICL) was used to address past problems with ML to develop a more efficient feature selection process and address the imbalance problem in all genomic data sets. The power of this novel ML approach and our ability to predict epigenetic phenomena and associated disease is suggested. The current approach requires extensive computation of features over the genome. A promising new approach is to introduce Deep Learning (DL) for the generation and simultaneous computation of novel genomic features tuned to the classification task. This approach can be used with any genomic or biological data set applied to medicine. The application of molecular epigenetic data in advanced machine learning analysis to medicine is the focus of this review.
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Details
- Title
- Machine learning for epigenetics and future medical applications
- Creators
- Lawrence B Holder - School of Electrical Engineering and Computer Science, Washington State UniversityM. Muksitul Haque - Center for Reproductive Biology, School of Biological Sciences, Washington State UniversityMichael K Skinner - Center for Reproductive Biology, School of Biological Sciences, Washington State University
- Publication Details
- Epigenetics, Vol.12(7), pp.505-514
- Academic Unit
- Biological Sciences, School of; Electrical Engineering and Computer Science, School of
- Publisher
- Taylor & Francis
- Grant note
- ES012974–10 / NIEHS NIH HHS R01 ES012974 / NIEHS NIH HHS
- Identifiers
- 99900546938001842
- Language
- English
- Resource Type
- Journal article