DeepCOP: deep learning-based approach to predict gene regulating effects of small molecules.

Journal: Bioinformatics (Oxford, England)
Published Date: Feb 1, 2020

Abstract

MOTIVATION: Recent advances in the areas of bioinformatics and chemogenomics are poised to accelerate the discovery of small molecule regulators of cell development. Combining large genomics and molecular data sources with powerful deep learning techniques has the potential to revolutionize predictive biology. In this study, we present Deep gene COmpound Profiler (DeepCOP), a deep learning based model that can predict gene regulating effects of low-molecular weight compounds. This model can be used for direct identification of a drug candidate causing a desired gene expression response, without utilizing any information on its interactions with protein target(s).

Authors

  • Godwin Woo
    Vancouver Prostate Centre, Department of Urologic Sciences , Faculty of Medicine, University of British Columbia , 2660 Oak Street , Vancouver , British Columbia V6H 3Z6 , Canada.
  • Michael Fernandez
    Data61, CSIRO , 343 Royal Parade, Parkville, Victoria 3052, Australia.
  • Michael Hsing
    Vancouver Prostate Centre, Department of Urologic Sciences , Faculty of Medicine, University of British Columbia , 2660 Oak Street , Vancouver , British Columbia V6H 3Z6 , Canada.
  • Nathan A Lack
    Department of Urologic Sciences, Faculty of Medicine, Vancouver Prostate Centre, University of British Columbia, Vancouver, British Columbia V6H 3Z6, Canada.
  • Ayse Derya Cavga
    Chemical and Biological Engineering, Koç University, Rumelifeneri Yolu, Istanbul 34450, Turkey.
  • Artem Cherkasov
    Vancouver Prostate Centre, Department of Urologic Sciences , Faculty of Medicine, University of British Columbia , 2660 Oak Street , Vancouver , British Columbia V6H 3Z6 , Canada.

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