Coev2Net: a computational framework for boosting confidence in high-throughput protein-protein interaction datasets
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 add...
| Main Authors: | , , , , , , , |
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| Other Authors: | |
| Format: | Article |
| Language: | English |
| Published: |
BioMed Central Ltd
2013
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| Online Access: | http://hdl.handle.net/1721.1/77555 https://orcid.org/0000-0002-2724-7228 |
| Summary: | 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. |
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