A computational framework for boosting confidence in high-throughput protein-protein interaction datasets
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CitationHosur, Raghavendra, Jian Peng, Arunachalam Vinayagam, Ulrich Stelzl, Jinbo Xu, Norbert Perrimon, Jadwiga Bienkowska, and Bonnie Berger. 2012. “A computational framework for boosting confidence in high-throughput protein-protein interaction datasets.” Genome Biology 13 (8): R76. doi:10.1186/gb-2012-13-8-r76. http://dx.doi.org/10.1186/gb-2012-13-8-r76.
AbstractImproving 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.
Citable link to this pagehttp://nrs.harvard.edu/urn-3:HUL.InstRepos:12406688
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