Predicting co-complexed protein pairs using genomic and proteomic data integration

Background: Identifying all protein-protein interactions in an organism is a major objective of proteomics. A related goal is to know which protein pairs are present in the same protein complex. High-throughput methods such as yeast two-hybrid (Y2H) and affinity purification coupled with mass spectrometry (APMS) have been used to detect interacting proteins on a genomic scale. However, both Y2H and APMS methods have substantial false-positive rates. Aside from highthroughput interaction screens, other gene- or protein-pair characteristics may also be informative of physical interaction. Therefore it is desirable to integrate multiple datasets and utilize their different predictive value for more accurate prediction of co-complexed relationship. Results: Using a supervised machine learning approach

• Type: Journal
• Year: 2004
• Where: BMCBI
• Authors: Lan V. Zhang, Sharyl L. Wong, Oliver D. King, Frederick P. Roth
• Keywords: BMCBI 2004, Predictions, Protein-pair Characteristics, Proteins