MOLI: multi-omics late integration with deep neural networks for drug response prediction.

Journal: Bioinformatics (Oxford, England)
Published Date:

Abstract

MOTIVATION: Historically, gene expression has been shown to be the most informative data for drug response prediction. Recent evidence suggests that integrating additional omics can improve the prediction accuracy which raises the question of how to integrate the additional omics. Regardless of the integration strategy, clinical utility and translatability are crucial. Thus, we reasoned a multi-omics approach combined with clinical datasets would improve drug response prediction and clinical relevance.

Authors

  • Hossein Sharifi-Noghabi
    School of Computing Science, Simon Fraser University, Burnaby, BC, Canada.
  • Olga Zolotareva
    International Research Training Group Computational Methods for the Analysis of the Diversity and Dynamics of Genomes and Genome Informatics, Faculty of Technology and Center for Biotechnology, Bielefeld University, Germany.
  • Colin C Collins
    Vancouver Prostate Centre, Vancouver, BC, Canada.
  • Martin Ester
    School of Computing Science, Simon Fraser University, Burnaby, BC, Canada.