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dc.contributor.authorLinghu, Bolanen_US
dc.contributor.authorSnitkin, Evan S.en_US
dc.contributor.authorHolloway, Dustin T.en_US
dc.contributor.authorGustafson, Adam M.en_US
dc.contributor.authorXia, Yuen_US
dc.contributor.authorDeLisi, Charlesen_US
dc.date.accessioned2012-01-11T21:09:34Z
dc.date.available2012-01-11T21:09:34Z
dc.date.copyright2008
dc.date.issued2008-2-25
dc.identifier.citationLinghu, Bolan, Evan S Snitkin, Dustin T Holloway, Adam M Gustafson, Yu Xia, Charles DeLisi. "High-precision high-coverage functional inference from integrated data sources" BMC Bioinformatics 9:119. (2008)
dc.identifier.issn1471-2105
dc.identifier.urihttp://hdl.handle.net/2144/3195
dc.description.abstractBACKGROUND. Information obtained from diverse data sources can be combined in a principled manner using various machine learning methods to increase the reliability and range of knowledge about protein function. The result is a weighted functional linkage network (FLN) in which linked neighbors share at least one function with high probability. Precision is, however, low. Aiming to provide precise functional annotation for as many proteins as possible, we explore and propose a two-step framework for functional annotation (1) construction of a high-coverage and reliable FLN via machine learning techniques (2) development of a decision rule for the constructed FLN to optimize functional annotation. RESULTS. We first apply this framework to Saccharomyces cerevisiae. In the first step, we demonstrate that four commonly used machine learning methods, Linear SVM, Linear Discriminant Analysis, Naïve Bayes, and Neural Network, all combine heterogeneous data to produce reliable and high-coverage FLNs, in which the linkage weight more accurately estimates functional coupling of linked proteins than use individual data sources alone. In the second step, empirical tuning of an adjustable decision rule on the constructed FLN reveals that basing annotation on maximum edge weight results in the most precise annotation at high coverages. In particular at low coverage all rules evaluated perform comparably. At coverage above approximately 50%, however, they diverge rapidly. At full coverage, the maximum weight decision rule still has a precision of approximately 70%, whereas for other methods, precision ranges from a high of slightly more than 30%, down to 3%. In addition, a scoring scheme to estimate the precisions of individual predictions is also provided. Finally, tests of the robustness of the framework indicate that our framework can be successfully applied to less studied organisms. CONCLUSION. We provide a general two-step function-annotation framework, and show that high coverage, high precision annotations can be achieved by constructing a high-coverage and reliable FLN via data integration followed by applying a maximum weight decision rule.en_US
dc.description.sponsorshipPhRMA Foundation (Research Starter Grant)en_US
dc.language.isoenen_US
dc.publisherBioMed Centralen_US
dc.rightsCopyright 2008 Linghu et al; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.en_US
dc.rights.urihttp://creativecommons.org/licenses/by/2.0
dc.titleHigh-Precision High-Coverage Functional Inference from Integrated Data Sourcesen_US
dc.typearticleen_US
dc.identifier.doi10.1186/1471-2105-9-119
dc.identifier.pubmedid18298847
dc.identifier.pmcid2292694


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Copyright 2008 Linghu et al; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Except where otherwise noted, this item's license is described as Copyright 2008 Linghu et al; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.