Efficient Drug-Pathway Association Analysis via Integrative Penalized Matrix Decomposition

Cong Li, Can YANG, Greg Hather, Ray Liu, Hongyu Zhao

Research output: Contribution to journalArticlepeer-review

5 Citations (Scopus)

Abstract

Traditional drug discovery practice usually follows the 'one drug - one target' approach, seeking to identify drug molecules that act on individual targets, which ignores the systemic nature of human diseases. Pathway-based drug discovery recently emerged as an appealing approach to overcome this limitation. An important first step of such pathway-based drug discovery is to identify associations between drug molecules and biological pathways. This task has been made feasible by the accumulating data from high-throughput transcription and drug sensitivity profiling. In this paper, we developed 'iPaD', an integrative Penalized Matrix Decomposition method to identify drug-pathway associations through jointly modeling of such high-throughput transcription and drug sensitivity data. A scalable bi-convex optimization algorithm was implemented and gave iPaD tremendous advantage in computational efficiency over current state-of-the-art method, which allows it to handle the ever-growing large-scale data sets that current method cannot afford to. On two widely used real data sets, iPaD also significantly outperformed the current method in terms of the number of validated drug-pathway associations that were identified.

Original languageEnglish
Article number7172456
Pages (from-to)531-540
Number of pages10
JournalIEEE/ACM Transactions on Computational Biology and Bioinformatics
Volume13
Issue number3
DOIs
Publication statusPublished - 1 May 2016

Scopus Subject Areas

  • Biotechnology
  • Genetics
  • Applied Mathematics

User-Defined Keywords

  • drug discovery
  • high dimensional data
  • latent factor model
  • matrix decomposition
  • penalized method
  • template

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