| Affiliation: | (1) Department of Mathematics, Washington University in St. Louis, St. Louis, Washington, MO 63130, USA;(2) Division of Biostatistics, School of Public Health, University of Minnesota, Minneapolis, MN 55455, USA;(3) Computational Biology Center, Memorial Sloan-Kettering Cancer Center, New York, NY 10021, USA;(4) Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT 06520, USA;(5) Department of Computer Science, Yale University, New Haven, CT 06520, USA;(6) Department of Epidemiology and Public Health, Yale University School of Medicine, New Haven, CT 06520, USA;(7) Department of Genetics, Yale University School of Medicine, New Haven, CT 06520, USA |
| Abstract: | Background Identifying protein-protein interactions is fundamental for understanding the molecular machinery of the cell. Proteome-wide studies of protein-protein interactions are of significant value, but the high-throughput experimental technologies suffer from high rates of both false positive and false negative predictions. In addition to high-throughput experimental data, many diverse types of genomic data can help predict protein-protein interactions, such as mRNA expression, localization, essentiality, and functional annotation. Evaluations of the information contributions from different evidences help to establish more parsimonious models with comparable or better prediction accuracy, and to obtain biological insights of the relationships between protein-protein interactions and other genomic information. |
