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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.<\/jats:p>\n                     <\/jats:sec><jats:sec>\n                        <jats:title>Results<\/jats:title>\n                        <jats:p>In this study, we use a combination of 3 genomic features \u2013 structurally known interacting Pfam domains, Gene Ontology annotations and sequence homology \u2013 as a means to assign reliability to the protein-protein interactions in <jats:italic>Saccharomyces cerevisiae<\/jats:italic> 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 <jats:italic>Saccharomyces cerevisiae<\/jats:italic> have high reliability. We use the method to estimate the number of true interactions in the high-throughput protein-protein interaction data sets in <jats:italic>Caenorhabditis elegans<\/jats:italic>, <jats:italic>Drosophila melanogaster<\/jats:italic> and <jats:italic>Homo sapiens<\/jats:italic> to be 27%, 18% and 68% respectively. Our results are available for searching and downloading at <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" ext-link-type=\"uri\" xlink:href=\"http:\/\/helix.protein.osaka-u.ac.jp\/htp\/\">http:\/\/helix.protein.osaka-u.ac.jp\/htp\/<\/jats:ext-link>.<\/jats:p>\n                     <\/jats:sec><jats:sec>\n                        <jats:title>Conclusion<\/jats:title>\n                        <jats: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.<\/jats:p>\n                     <\/jats:sec>","DOI":"10.1186\/1471-2105-6-100","type":"journal-article","created":{"date-parts":[[2005,4,19]],"date-time":"2005-04-19T06:14:05Z","timestamp":1113891245000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":128,"title":["Filtering high-throughput protein-protein interaction data using a combination of genomic features"],"prefix":"10.1186","volume":"6","author":[{"given":"Ashwini","family":"Patil","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Haruki","family":"Nakamura","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2005,4,18]]},"reference":[{"issue":"6887","key":"425_CR1","doi-asserted-by":"publisher","first-page":"399","DOI":"10.1038\/nature750","volume":"417","author":"C von Mering","year":"2002","unstructured":"von Mering C, Krause R, Snel B, Cornell M, Oliver SG, Fields S, Bork P: Comparative assessment of large-scale data sets of protein-protein interactions. 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