Kuchaiev, O. and Rasajski, M. and Higham, D.J. and Przulj, N. (2009) Geometric de-noising of protein-protein interaction networks. PLOS Computational Biology, 5 (8). pp. 1-10. ISSN 1553-734XFull text not available in this repository. (Request a copy from the Strathclyde author)
Understanding complex networks of protein-protein interactions (PPIs) is one of the foremost challenges of the postgenomic 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 in the human PPI network, a statistically significant fraction of which correspond to protein pairs sharing common GO terms. Moreover, we validate a statistically significant portion of our predicted interactions in the HPRD database and the newer release of BioGRID. The data and Matlab code implementing the methods are freely available from the web site: http://www.kuchaev.com/Denoising.
|Keywords:||geometric de-noising, protein-protein interaction networks, PPIs, Probabilities. Mathematical statistics, Ecology, Cellular and Molecular Neuroscience, Ecology, Evolution, Behavior and Systematics, Genetics, Modelling and Simulation, Computational Theory and Mathematics, Molecular Biology|
|Subjects:||Science > Mathematics > Probabilities. Mathematical statistics|
|Department:||Faculty of Science > Mathematics and Statistics|
|Depositing user:||Strathprints Administrator|
|Date Deposited:||18 Jun 2010 13:01|
|Last modified:||02 Dec 2016 03:30|