A computational framework for boosting confidence in high-throughput protein-protein interaction datasets

Raghavendra Hosur, Jian Peng, Arunachalam Vinayagam, Ulrich Stelzl, Jinbo Xu, Norbert Perrimon, Jadwiga Bienkowska, Bonnie Berger

Research output: Contribution to journalArticlepeer-review

Abstract

Improving the quality and coverage of the protein interactome is of tantamount importance for biomedical research, particularly given the various sources of uncertainty in high-throughput techniques. We introduce a structure-based framework, Coev2Net, for computing a single confidence score that addresses both false-positive and false-negative rates. Coev2Net is easily applied to thousands of binary protein interactions and has superior predictive performance over existing methods. We experimentally validate selected high-confidence predictions in the human MAPK network and show that predicted interfaces are enriched for cancer -related or damaging SNPs. Coev2Net can be downloaded at http://struct2net.csail.mit.edu.

Original languageEnglish (US)
Pages (from-to)R76
JournalGenome biology
Volume13
Issue number8
DOIs
StatePublished - 2012
Externally publishedYes

ASJC Scopus subject areas

  • Ecology, Evolution, Behavior and Systematics
  • Genetics
  • Cell Biology

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