title: 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.
authors.forename: Raghavendra
authors.initials: R
authors.lastname: Hosur
authors.forename: Jian
authors.initials: J
authors.lastname: Peng
authors.forename: Arunachalam
authors.initials: A
authors.lastname: Vinayagam
authors.forename: Ulrich
authors.initials: U
authors.lastname: Stelzl
authors.forename: Jinbo
authors.initials: J
authors.lastname: Xu
authors.forename: Norbert
authors.initials: N
authors.lastname: Perrimon
authors.forename: Jadwiga
authors.initials: J
authors.lastname: Bienkowska
authors.forename: Bonnie
authors.initials: B
authors.lastname: Berger
mesh_terms: D000465:Algorithms; D019295:Computational Biology / Q000379:methods*; D030562:Databases, Protein; D057166:High-Throughput Screening Assays; D006801:Humans; D020928:Mitogen-Activated Protein Kinases / Q000235:genetics / Q000378:metabolism; D020125:Mutation, Missense; D020641:Polymorphism, Single Nucleotide; D011487:Protein Conformation; D025941:Protein Interaction Mapping / Q000379:methods*; D015203:Reproducibility of Results; D012984:Software
chemical_list: D020928:Mitogen-Activated Protein Kinases