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Journal Article

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

MPS-Authors
http://pubman.mpdl.mpg.de/cone/persons/resource/persons50573

Stelzl,  U.
Molecular Interaction Networks (Ulrich Stelzl), Independent Junior Research Groups (OWL), Max Planck Institute for Molecular Genetics, Max Planck Society;

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Citation

Hosur, R., Peng, J., Vinayagam, A., Stelzl, U., Xu, J., Perrimon, N., et al. (2012). A computational framework for boosting confidence in high-throughput protein-protein interaction datasets. Genome Biology, 13(8), R76-R76. doi:10.1186/gb-2012-13-8-r76.


Cite as: http://hdl.handle.net/11858/00-001M-0000-000E-E7AC-7
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.