Information assessment on predicting protein-protein interactions

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. Results: Our assessment is based on the genomic features used in a Bayesian network approach to predict protein-protein interactions genome-wide in yeast. In the special cas...

• Type: Journal
• Year: 2004
• Where: BMCBI
• Authors: Nan Lin, Baolin Wu, Ronald Jansen, Mark Gerstein, Hongyu Zhao
• Keywords: BMCBI 2004, Genomic Data, Information, Protein-protein Interactions