Large-scale integration of heterogeneous pharmacogenomic data for identifying drug mechanism of action

Yunan Luo, Sheng Wang, Jinfeng Xiao, Jian Peng

Research output: Contribution to journalConference article

Abstract

A variety of large-scale pharmacogenomic data, such as perturbation experiments and sensitivity profiles, enable the systematical identification of drug mechanism of actions (MoAs), which is a crucial task in the era of precision medicine. However, integrating these complementary pharmacogenomic datasets is inherently challenging due to the wild heterogeneity, high-dimensionality and noisy nature of these datasets. In this work, we develop Mania, a novel method for the scalable integration of large-scale pharmacogenomic data. Mania first constructs a drug-drug similarity network through integrating multiple heterogeneous data sources, including drug sensitivity, drug chemical structure, and perturbation assays. It then learns a compact vector representation for each drug to simultaneously encode its structural and pharmacogenomic properties. Extensive experiments demonstrate that Mania achieves substantially improved performance in both MoAs and targets prediction, compared to predictions based on individual data sources as well as a state-of-the-art integrative method. Moreover, Mania identifies drugs that target frequently mutated cancer genes, which provides novel insights into drug repurposing.

Original languageEnglish (US)
Pages (from-to)44-55
Number of pages12
JournalPacific Symposium on Biocomputing
Volume0
Issue number212669
DOIs
StatePublished - Jan 1 2018
Event23rd Pacific Symposium on Biocomputing, PSB 2018 - Kohala Coast, United States
Duration: Jan 3 2018Jan 7 2018

Keywords

  • Data integration
  • Dimensionality reduction
  • Drug mechanisms of action
  • Drug similarity network
  • Drug target

ASJC Scopus subject areas

  • Biomedical Engineering
  • Computational Theory and Mathematics

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