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

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.

SUBMITTER: Hosur R

PROVIDER: S-EPMC4053744 | biostudies-literature | 2012 Aug

REPOSITORIES: biostudies-literature

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

Hosur Raghavendra R Peng Jian J Vinayagam Arunachalam A Stelzl Ulrich U Xu Jinbo J Perrimon Norbert N Bienkowska Jadwiga J Berger Bonnie B

Genome biology 20120831 8

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 experiment ...[more]

PMID: 22937800

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