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

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.

journal_name

Genome Biol

journal_title

Genome biology

authors

Hosur R,Peng J,Vinayagam A,Stelzl U,Xu J,Perrimon N,Bienkowska J,Berger B

doi

10.1186/gb-2012-13-8-r76

subject

Has Abstract

pub_date

2012-08-31 00:00:00

pages

R76

issue

8

eissn

1474-7596

issn

1474-760X

pii

gb-2012-13-8-r76

journal_volume

13

pub_type

杂志文章

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