Coev2Net: a computational framework for boosting confidence in high-throughput protein-protein interaction datasets
| Autor: | Hosur, Raghavendra, Peng, Jian, Vinayagam, Arunachalam, Stelzl, Ulrich, Xu, Jinbo, Perrimon, Norbert, Bienkowska, Jadwiga R., Berger, Bonnie |
|---|---|
| Přispěvatelé: | Hosur, Raghavendra, Peng, Jian, Berger, Bonnie |
| Jazyk: | angličtina |
| Rok vydání: | 2012 |
| Zdroj: | BioMed Central Ltd |
| Popis: | 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. National Institutes of Health (U.S.) (Grant R01GM081871) |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|