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dc.contributor.authorGresock, Josephen_US
dc.contributor.authorKumar, Deepten_US
dc.contributor.authorHelm, Richarden_US
dc.contributor.authorPotts, Malcolmen_US
dc.contributor.authorRamakrishnan, Narenen_US
dc.date.accessioned2013-06-19T14:37:05Z
dc.date.available2013-06-19T14:37:05Z
dc.date.issued2007
dc.identifierhttp://eprints.cs.vt.edu/archive/00000943/en_US
dc.identifier.urihttp://hdl.handle.net/10919/19641
dc.description.abstractMotivation: There are now a multitude of articles published in a diversity of journals providing information about genes, proteins, pathways, and entire processes. Each article investigates particular subsets of a biological process, but to gain insight into the functioning of a system as a whole, we must computationally integrate information across multiple publications. This is especially important in problems such as modeling cross-talk in signaling networks, designing drug therapies for combinatorial selectivity, and unraveling the role of gene interactions in deleterious phenotypes, where the cost of performing combinatorial screens is exorbitant. Results: We present an automated approach to biological knowledge discovery from PubMed abstracts, suitable for unraveling combinatorial relationships. It involves the systematic application of a `storytelling' algorithm followed by compression of the stories into `novellas.' Given a start and end publication, typically with little or no overlap in content, storytelling identifies a chain of intermediate publications from one to the other, such that neighboring publications have significant content similarity. Stories discovered thus provide an argued approach to relate distant concepts through compositions of related concepts. The chains of links employed by stories are then mined to find frequently reused sub-stories, which can be compressed to yield novellas, or compact templates of connections. We demonstrate a successful application of storytelling and novella finding to modeling combinatorial relationships between introduction of extracellular factors and downstream cellular events. Availability: A story visualizer, suitable for interactive exploration of stories and novellas described in this paper, is available for demo/download at https://bioinformatics.cs.vt.edu/storytelling.en_US
dc.format.mimetypeapplication/pdfen_US
dc.publisherDepartment of Computer Science, Virginia Polytechnic Institute & State Universityen_US
dc.subjectInformation retrievalen_US
dc.subjectBioinformaticsen_US
dc.titleMining Novellas from PubMed Abstracts using a Storytelling Algorithmen_US
dc.typeTechnical reporten_US
dc.identifier.trnumberTR-07-08en_US
dc.type.dcmitypeTexten_US
dc.identifier.sourceurlhttp://eprints.cs.vt.edu/archive/00000943/01/story.pdf


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