Probabilistic model of the human protein-protein interaction network

• Published in: Nature biotechnology Vol. 23; no. 8; pp. 951 - 959
• Main Authors: Chinnaiyan, Arul M, Rhodes, Daniel R, Tomlins, Scott A, Varambally, Sooryanarayana, Mahavisno, Vasudeva, Barrette, Terrence, Kalyana-Sundaram, Shanker, Ghosh, Debashis, Pandey, Akhilesh
• Format: Journal Article
• Language: English
• Published: New York, NY Nature 01.08.2005; Nature Publishing Group
• Subjects: Biological and medical sciences; Biotechnology; Chromosome Mapping - methods; Computer Simulation; Deregulation; Fundamental and applied biological sciences. Psychology; Gene Expression Profiling - methods; Gene Expression Regulation, Neoplastic; General aspects; Genome, Human; Humans; Mathematics in biology. Statistical analysis. Models. Metrology. Data processing in biology (general aspects); Models, Biological; Models, Statistical; Neoplasm Proteins - genetics; Neoplasm Proteins - metabolism; Neoplasms - genetics; Neoplasms - metabolism; Prediction models; Protein Interaction Mapping - methods; Proteome - genetics; Proteome - metabolism; Scientists; Signal Transduction; Human; Disease; Genomics; Molecular interaction; Probabilistic model; Malignant tumor; Gene expression; Modeling; Protein
• ISSN: 1087-0156; 1546-1696
• DOI: 10.1038/nbt1103

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.