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dc.contributor.authorJin, Yingen_US
dc.contributor.authorMurali, T. M.en_US
dc.contributor.authorRamakrishnan, Narenen_US
dc.date.accessioned2013-06-19T14:36:03Z
dc.date.available2013-06-19T14:36:03Z
dc.date.issued2007-08-01
dc.identifierhttp://eprints.cs.vt.edu/archive/00000988/en_US
dc.identifier.urihttp://hdl.handle.net/10919/19820
dc.description.abstractHigh-throughput biological screens are yielding ever-growing streams of information about multiple aspects of cellular activity. As more and more categories of datasets come online, there is a corresponding multitude of ways in which inferences can be chained across them, motivating the need for compositional data mining algorithms. In this paper, we argue that such compositional data mining can be effectively realized by functionally cascading redescription mining and biclustering algorithms as primitives. Both these primitives mirror shifts of vocabulary that can be composed in arbitrary ways to create rich chains of inferences. Given a relational database and its schema, we show how the schema can be automatically compiled into a compositional data mining program, and how different domains in the schema can be related through logical sequences of biclustering and redescription invocations. This feature allows us to rapidly prototype new data mining applications, yielding greater understanding of scientific datasets. We describe two applications of compositional data mining: (i) matching terms across categories of the Gene Ontology and (ii) understanding the molecular mechanisms underlying stress response in human cells.en_US
dc.format.mimetypeapplication/pdfen_US
dc.publisherDepartment of Computer Science, Virginia Polytechnic Institute & State Universityen_US
dc.relation.ispartofComputer Science Technical Reportsen_US
dc.subjectInformation retrievalen_US
dc.subjectBioinformaticsen_US
dc.titleCompositional Mining of Multi-Relational Biological Datasetsen_US
dc.typeTechnical reporten_US
dc.identifier.trnumberTR-07-29en_US
dc.type.dcmitypeTexten_US
dc.identifier.sourceurlhttp://eprints.cs.vt.edu/archive/00000988/01/CDM.pdf


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