MethylNet: an automated and modular deep learning approach for DNA methylation analysis.

Journal: BMC bioinformatics
Published Date: Mar 17, 2020

Abstract

BACKGROUND: DNA methylation (DNAm) is an epigenetic regulator of gene expression programs that can be altered by environmental exposures, aging, and in pathogenesis. Traditional analyses that associate DNAm alterations with phenotypes suffer from multiple hypothesis testing and multi-collinearity due to the high-dimensional, continuous, interacting and non-linear nature of the data. Deep learning analyses have shown much promise to study disease heterogeneity. DNAm deep learning approaches have not yet been formalized into user-friendly frameworks for execution, training, and interpreting models. Here, we describe MethylNet, a DNAm deep learning method that can construct embeddings, make predictions, generate new data, and uncover unknown heterogeneity with minimal user supervision.

Authors

  • Joshua J Levy
    DOE Joint Genome Institute, 2800 Mitchell Dr, Walnut Creek, CA, 94598, USA.
  • Alexander J Titus
    Department of Defense, Office of the Under Secretary of Defense for Research & Engineering, Washington, DC, USA.
  • Curtis L Petersen
    Catherine H. Saunders, Curtis L. Petersen, Marie-Anne Durand, and Glyn Elwyn, The Dartmouth Institute for Health Policy & Clinical Practice; Curtis L. Petersen, Geisel School of Medicine at Dartmouth; and Pamela J. Bagley, Dartmouth College, Lebanon, NH.
  • Youdinghuan Chen
    Department of Epidemiology, Lebanon, USA.
  • Lucas A Salas
    Department of Epidemiology, Lebanon, USA.
  • Brock C Christensen
    Department of Epidemiology, Lebanon, USA. Brock.C.Christensen@dartmouth.edu.

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