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Proteins do not exclusively interact in pairs as the final biological function of a protein in the cellular context is often hold by a group of proteins. In order to accurately infer modules of functionally interacting proteins, the consideration of not only direct but also indirect relationships is required.<\/jats:p>\n            <jats:p>In this paper, we used the Bond Energy Algorithm (<jats:italic>BEA<\/jats:italic>) to predict functionally related groups of proteins. With <jats:italic>BEA<\/jats:italic> we create clusters of phylogenetic profiles based on the associations of the surrounding elements of the analyzed data using a metric that considers linked relationships among elements in the data set.<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>Results<\/jats:title>\n            <jats:p>Using phylogenetic profiles obtained from the Cluster of Orthologous Groups of Proteins (<jats:italic>COG<\/jats:italic>) database, we conducted a series of clustering experiments using <jats:italic>BEA<\/jats:italic> to predict (upper level) relationships between profiles. We evaluated our results by comparing with <jats:italic>COG's<\/jats:italic> functional categories, And even more, with the experimentally determined functional relationships between proteins provided by the <jats:italic>DIP<\/jats:italic> and <jats:italic>ECOCYC<\/jats:italic> databases. Our results demonstrate that <jats:italic>BEA<\/jats:italic> is capable of predicting meaningful modules of functionally related proteins. <jats:italic>BEA<\/jats:italic> outperforms traditionally used clustering methods, such as <jats:italic>k<\/jats:italic>-means and hierarchical clustering by predicting functional relationships between proteins with higher accuracy.<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>Conclusion<\/jats:title>\n            <jats:p>This study shows that the linked relationships of phylogenetic profiles obtained by <jats:italic>BEA<\/jats:italic> is useful for detecting functional associations between profiles and extending functional modules not found by traditional methods. <jats:italic>BEA<\/jats:italic> is capable of detecting relationship among phylogenetic patterns by linking them through a common element shared in a group. Additionally, we discuss how the proposed method may become more powerful if other criteria to classify different levels of protein functional interactions, as gene neighborhood or protein fusion information, is provided.<\/jats:p>\n          <\/jats:sec>","DOI":"10.1186\/1471-2105-9-285","type":"journal-article","created":{"date-parts":[[2008,6,18]],"date-time":"2008-06-18T06:11:23Z","timestamp":1213769483000},"update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["Inferring modules of functionally interacting proteins using the Bond Energy Algorithm"],"prefix":"10.1186","volume":"9","author":[{"given":"Ryosuke LA","family":"Watanabe","sequence":"first","affiliation":[]},{"given":"Enrique","family":"Morett","sequence":"additional","affiliation":[]},{"given":"Edgar E","family":"Vallejo","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2008,6,17]]},"reference":[{"key":"2270_CR1","doi-asserted-by":"publisher","first-page":"D431","DOI":"10.1093\/nar\/gkh081","volume":"D","author":"I Schomburg","year":"2004","unstructured":"Schomburg I, Chang A, Ebeling C, Gremse M, Huhn HeldtGC, Schomburg D: BRENDA, the enzyme database: updates and major new developments. 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