Multitask Matrix Completion for Learning Protein Interactions Across Diseases

Meghana Kshirsagar, Keerthiram Murugesan, Jaime G. Carbonell, Judith Klein-Seetharaman

Research output: Contribution to journalArticlepeer-review

4 Scopus citations

Abstract

Disease-causing pathogens such as viruses introduce their proteins into the host cells in which they interact with the host's proteins, enabling the virus to replicate inside the host. These interactions between pathogen and host proteins are key to understanding infectious diseases. Often multiple diseases involve phylogenetically related or biologically similar pathogens. Here we present a multitask learning method to jointly model interactions between human proteins and three different but related viruses: Hepatitis C, Ebola virus, and Influenza A. Our multitask matrix completion-based model uses a shared low-rank structure in addition to a task-specific sparse structure to incorporate the various interactions. We obtain between 7 and 39 percentage points improvement in predictive performance over prior state-of-the-art models. We show how our model's parameters can be interpreted to reveal both general and specific interaction-relevant characteristics of the viruses. Our code is available online.

Original languageEnglish (US)
Pages (from-to)501-514
Number of pages14
JournalJournal of Computational Biology
Volume24
Issue number6
DOIs
StatePublished - Jun 2017
Externally publishedYes

Keywords

  • Host-pathogen protein-protein interaction
  • matrix completion
  • multitask learning
  • protein interaction prediction
  • viruses

ASJC Scopus subject areas

  • Modeling and Simulation
  • Molecular Biology
  • Genetics
  • Computational Mathematics
  • Computational Theory and Mathematics

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