| Summary: | <p>Abstract</p> <p>Background</p> <p>Protein-protein interaction data used in the creation or prediction of molecular networks is usually obtained from large scale or high-throughput experiments. This experimental data is liable to contain a large number of spurious interactions. Hence, there is a need to validate the interactions and filter out the incorrect data before using them in prediction studies.</p> <p>Results</p> <p>In this study, we use a combination of 3 genomic features – structurally known interacting Pfam domains, Gene Ontology annotations and sequence homology – as a means to assign reliability to the protein-protein interactions in <it>Saccharomyces cerevisiae </it>determined by high-throughput experiments. Using Bayesian network approaches, we show that protein-protein interactions from high-throughput data supported by one or more genomic features have a higher likelihood ratio and hence are more likely to be real interactions. Our method has a high sensitivity (90%) and good specificity (63%). We show that 56% of the interactions from high-throughput experiments in <it>Saccharomyces cerevisiae </it>have high reliability. We use the method to estimate the number of true interactions in the high-throughput protein-protein interaction data sets in <it>Caenorhabditis elegans</it>, <it>Drosophila melanogaster </it>and <it>Homo sapiens </it>to be 27%, 18% and 68% respectively. Our results are available for searching and downloading at <url>http://helix.protein.osaka-u.ac.jp/htp/</url>.</p> <p>Conclusion</p> <p>A combination of genomic features that include sequence, structure and annotation information is a good predictor of true interactions in large and noisy high-throughput data sets. The method has a very high sensitivity and good specificity and can be used to assign a likelihood ratio, corresponding to the reliability, to each interaction.</p>
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