geometric de-noising of protein-protein interaction networks蛋白质相互作用网络的几何去噪.pdfVIP

Geometric De-noising of Protein-Protein Interaction Networks 1 ˇ 1,2 3 ˇ ˇ 1 Oleksii Kuchaiev , Marija Rasajski , Desmond J. Higham , Natasa Przulj * 1 Department of Computer Science, University of California, Irvine, California, United States of America, 2 Faculty of Electrical Engineering, University of Belgrade, Belgrade, Serbia, 3 Department of Mathematics, University of Strathclyde, Glasgow, United Kingdom Abstract Understanding complex networks of protein-protein interactions (PPIs) is one of the foremost challenges of the post- genomic era. Due to the recent advances in experimental bio-technology, including yeast-2-hybrid (Y2H), tandem affinity purification (TAP) and other high-throughput methods for protein-protein interaction (PPI) detection, huge amounts of PPI network data are becoming available. Of major concern, however, are the levels of noise and incompleteness. For example, for Y2H screens, it is thought that the false positive rate could be as high as 64%, and the false negative rate may range from 43% to 71%. TAP experiments are believed to have comparable levels of noise. We present a novel technique to assess the confidence levels of interactions in PPI networks obtained from experimental studies. We use it for predicting new interactions and thus for guiding future biological experiments. This technique is the first to utilize currently the best fitting network model for PPI networks, geometric graphs. Our approach achieves specificity of 85% and sensitivity of 90%. We use it to assign confidence scores to physical protein-protein interactions in the human PPI network downloaded from BioGRID. Using our approach, we predict 251 interactions