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dc.contributor.authorLicamele, Louis
dc.contributor.authorGetoor, Lise
dc.date.accessioned2008-03-28T14:44:06Z
dc.date.available2008-03-28T14:44:06Z
dc.date.issued2007-01-07
dc.identifier.urihttp://hdl.handle.net/1903/7555
dc.description.abstractProteins play a fundamental role in ever y process within the cell. Understanding how proteins interact, and the functional units they are par t of, is important to furthering our knowledge of the entire biological process. There has been a growing amount of work, both experimental and computational, on determining the protein-protein interaction network. Recently researchers have had success looking at this as a relational learning problem. In this work, we further this investigation, proposing several novel relational features for predicting protein-protein interaction. These features can be used in any classifier. Our approach allows large and complex networks to be analyzed and is an alternative to using more expensive relational methods. We show that we are able to get an accuracy of 81.7% when predicting new links from noisy high throughput data.en
dc.format.extent412503 bytes
dc.format.mimetypeapplication/pdf
dc.language.isoen_USen
dc.relation.ispartofseriesUM Computer Science Departmenten
dc.relation.ispartofseriesCS-TR-4909en
dc.titlePredicting Protein-Protein Interactions Using Relational Featuresen
dc.typeTechnical Reporten


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