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

Mavaie, Pegah; Holder, Lawrence; Beck, Daniel; Skinner, Michael K

Mavaie, Pegah; Holder, Lawrence; Beck, Daniel; Skinner, Michael K.

Affiliation

Mavaie P; School of Electrical Engineering and Computer Science, Washington State University, Pullman, WA, 99164-2752, USA.
Holder L; School of Electrical Engineering and Computer Science, Washington State University, Pullman, WA, 99164-2752, USA. holder@wsu.edu.
Beck D; Center for Reproductive Biology, School of Biological Sciences, Washington State University, Pullman, WA, 99164-4236, USA.
Skinner MK; Center for Reproductive Biology, School of Biological Sciences, Washington State University, Pullman, WA, 99164-4236, USA. skinner@wsu.edu.

BMC Bioinformatics ; 22(1): 575, 2021 Nov 30.

Article in En | MEDLINE | ID: mdl-34847877

Subject(s)

Artificial Intelligence; DNA Methylation; Animals; DNA; Epigenesis, Genetic; Genome, Human; Humans; Machine Learning; Rats

Key words

Artificial intelligence; DNA methylation; Deep learning; Epigenetics; Epimutation; Machine learning; Transgenerational

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Full text: 1 Database: MEDLINE Main subject: Artificial Intelligence / DNA Methylation Type of study: Prognostic_studies / Risk_factors_studies Limits: Animals / Humans Language: En Journal: BMC Bioinformatics Journal subject: INFORMATICA MEDICA Year: 2021 Type: Article Affiliation country: United States

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