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

Journal: BMC bioinformatics
Published Date:

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.

Authors

  • Pegah Mavaie
    School of Electrical Engineering and Computer Science, Washington State University, Pullman, WA 99164-2752, USA.
  • Lawrence Holder
    School of Electrical Engineering and Computer Science, Washington State University, Pullman, WA 99164-2752, USA.
  • Daniel Beck
    Center for Reproductive Biology, School of Biological Sciences, Washington State University, Pullman, WA 99164-4236, USA.
  • Michael K Skinner
    Center for Reproductive Biology, School of Biological Sciences, Washington State University, Pullman, WA 99164-4236, USA.