Citation:
Hosur R, Peng J, Vinayagam A, Stelzl U, Xu J, Perrimon N, et al. A computational framework for boosting confidence in high-throughput protein-protein interaction datasets. Genome Biol. 2012;13 (8) :R76.
 | 2012_Genome Bio_Hosur.pdf | 1.59 MB |
| Supplement.pdf | 692 KB |
Date Published:
2012Abstract:
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.
See also: Research Article
