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dc.contributor.authorSefer, Emre
dc.contributor.authorKingsford, C.
dc.description.abstractComputational techniques have been successful at predicting protein function from relational data (functional or physical interactions). These techniques have been used to generate hypotheses and to direct experimental validation. With few exceptions, the task is modeled as multilabel classification problems where the labels (functions) are treated independently or semi-independently. However, databases such as the Gene Ontology provide information about the similarities between functions. We explore the use of the Metric Labeling combinatorial optimization problem to make use of heuristically computed distances between functions to make more accurate predictions of protein function in networks derived from both physical interactions and a combination of other data types. To do this, we give a new technique (based on convex optimization) for converting heuristic semimetric distances into a metric with minimum least-squared distortion (LSD). The Metric Labeling approach is shown to outperform five existing techniques for inferring function from networks. These results suggest that Metric Labeling is useful for protein function prediction, and that LSD minimization can help solve the problem of converting heuristic distances to a metric.en_US
dc.publisherMary Ann Liebert, Inc.en_US
dc.relation.ispartofJournal of Computational Biology
dc.titleMetric labeling and semimetric embedding for protein annotation predictionen_US
dc.publicationstatusPublished onlineen_US
dc.contributor.departmentÖzyeğin University
dc.contributor.authorID(ORCID 0000-0002-9186-0270 & YÖK ID ) Sefer, Emre
dc.subject.keywordsGene ontologyen_US
dc.subject.keywordsMetric labelingen_US
dc.subject.keywordsProtein function predictionen_US
dc.relation.publicationcategoryArticle - International Refereed Journal - Institutional Academic Staff

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