Finding Combinatorial Connections between Concepts in the Biomedical Literature

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dc.contributor.authorGresock, Joseph Aaronen
dc.contributor.committeechairRamakrishnan, Narenen
dc.contributor.committeememberHelm, Richard F.en
dc.contributor.committeememberMurali, T. M.en
dc.contributor.departmentComputer Scienceen
dc.date.accessioned2014-03-14T20:35:52Zen
dc.date.adate2007-05-11en
dc.date.available2014-03-14T20:35:52Zen
dc.date.issued2007-05-08en
dc.date.rdate2007-05-11en
dc.date.sdate2007-05-09en
dc.description.abstractThere 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. In this thesis, 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 a series of filtering and compression operations over the mined stories. 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 compact templates of connections. We demonstrate a successful application of storytelling to finding combinatorial connections between biological concepts using two application case studies.en
dc.description.degreeMaster of Scienceen
dc.identifier.otheretd-05092007-165611en
dc.identifier.sourceurlhttp://scholar.lib.vt.edu/theses/available/etd-05092007-165611/en
dc.identifier.urihttp://hdl.handle.net/10919/32441en
dc.publisherVirginia Techen
dc.relation.haspartthesis.pdfen
dc.rightsIn Copyrighten
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/en
dc.subjectliterature miningen
dc.subjectStorytellingen
dc.subjectstoriesen
dc.titleFinding Combinatorial Connections between Concepts in the Biomedical Literatureen
dc.typeThesisen
thesis.degree.disciplineComputer Scienceen
thesis.degree.grantorVirginia Polytechnic Institute and State Universityen
thesis.degree.levelmastersen
thesis.degree.nameMaster of Scienceen

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