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

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...

Full description

Bibliographic Details
Main Authors: Hosur, Raghavendra, Peng, Jian, Vinayagam, Arunachalam, Stelzl, Ulrich, Xu, Jinbo, Perrimon, Norbert, Bienkowska, Jadwiga, Berger, Bonnie
Format: Online
Language:English
Published: BioMed Central 2012
Online Access:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4053744/
Description
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. Coev2Net can be downloaded at http://struct2net.csail.mit.edu.

Similar Items