Probabilistic model of the human protein-protein interaction network

Daniel R. Rhodes, Scott A. Tomlins, Sooryanarayana Varambally, Vasudeva Mahavisno, Terrence Barrette, Shanker Kalyana-Sundaram, Debashis Ghosh, Akhilesh Pandey, Arul M. Chinnaiyan

Research output: Contribution to journalArticlepeer-review

327 Scopus citations

Abstract

A catalog of all human protein-protein interactions would provide scientists with a framework to study protein deregulation in complex diseases such as cancer. Here we demonstrate that a probabilistic analysis integrating model organism interactome data, protein domain data, genome-wide gene expression data and functional annotation data predicts nearly 40,000 protein-protein interactions in humans-a result comparable to those obtained with experimental and computational approaches in model organisms. We validated the accuracy of the predictive model on an independent test set of known interactions and also experimentally confirmed two predicted interactions relevant to human cancer, implicating uncharacterized proteins into definitive pathways. We also applied the human interactome network to cancer genomics data and identified several interaction subnetworks activated in cancer. This integrative analysis provides a comprehensive framework for exploring the human protein interaction network.

Original languageEnglish (US)
Pages (from-to)951-959
Number of pages9
JournalNature biotechnology
Volume23
Issue number8
DOIs
StatePublished - Aug 2005

ASJC Scopus subject areas

  • Biotechnology
  • Bioengineering
  • Applied Microbiology and Biotechnology
  • Molecular Medicine
  • Biomedical Engineering

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