Probabilistic model of the human protein-protein interaction network.
A catalog of all human protein-protein interactions would provide scientists with a framework to study protein deregulation in complex diseases such as cancer. Here we demonstrate that a probabilistic analysis integrating model organism interactome data, protein domain data, genome-wide gene expression data and functional annotation data predicts nearly 40,000 protein-protein interactions in humans-a result comparable to those obtained with experimental and computational approaches in model organisms. We validated the accuracy of the predictive model on an independent test set of known interactions and also experimentally confirmed two predicted interactions relevant to human cancer, implicating uncharacterized proteins into definitive pathways. We also applied the human interactome network to cancer genomics data and identified several interaction subnetworks activated in cancer. This integrative analysis provides a comprehensive framework for exploring the human protein interaction network.
Rhodes
Daniel R
DR
Bioinformatics Program, Department of Pathology, University of Michigan Medical School, Ann Arbor, Michigan 48109, USA.
Tomlins
Scott A
SA
Varambally
Sooryanarayana
S
Mahavisno
Vasudeva
V
Barrette
Terrence
T
Kalyana-Sundaram
Shanker
S
Ghosh
Debashis
D
Pandey
Akhilesh
A
Chinnaiyan
Arul M
AM
D002874:Chromosome Mapping / Q000379:methods; D003198:Computer Simulation; D020869:Gene Expression Profiling / Q000379:methods*; D015972:Gene Expression Regulation, Neoplastic; D015894:Genome, Human; D006801:Humans; D008954:Models, Biological; D015233:Models, Statistical; D009363:Neoplasm Proteins / Q000235:genetics / Q000378:metabolism*; D009369:Neoplasms / Q000235:genetics / Q000378:metabolism*; D025941:Protein Interaction Mapping / Q000379:methods*; D020543:Proteome / Q000235:genetics / Q000378:metabolism*; D015398:Signal Transduction
D009363:Neoplasm Proteins; D020543:Proteome