Coev2Net: a computational framework for boosting confidence in high-throughput protein-protein interaction datasets
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gb-2012-13-8-r76.pdf
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1.71 MB
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Checksum (MD5)
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Author(s)
Hosur, Raghavendra
Peng, Jian
Vinayagam, Arunachalam
Stelzl, Ulrich
Xu, Jinbo
Perrimon, Norbert
Bienkowska, Jadwiga R.
Berger, Bonnie
Date Issued
August 2012
Journal
Genome Biology
Publisher
BioMed Central Ltd
Citation
Hosur, Raghavendra et al. “A Computational Framework for Boosting Confidence in High-throughput Protein-protein Interaction Datasets.” Genome Biology 13.8 (2012).
Version
Final published version
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.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Massachusetts Institute of Technology. Department of Mathematics
Terms of Use
Creative Commons Attribution
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DOI of Published Version
https://doi.org/10.1186/gb-2012-13-8-r76
