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Biomedical Literature Mining with Transitive Closure and Maximum Network Flow

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dc.contributor.advisor Mukhopadhyay, Snehasis
dc.contributor.advisor Xia, Yuni
dc.contributor.advisor Fang, Shiafoen
dc.contributor.author Hoblitzell, Andrew P.
dc.date.accessioned 2011-07-11T20:19:06Z
dc.date.available 2011-07-11T20:19:06Z
dc.date.issued 2011-05-15
dc.identifier.citation Andrew Hoblitzell, Snehasis Mukhopadhyay, Qian You, Shiaofen Fang, Yuni Xia, and Joseph Bidwell. 2010. Text mining for bone biology. In Proceedings of the 19th ACM International Symposium on High Performance Distributed Computing (HPDC '10). ACM, New York, NY, USA, 522-530. DOI=10.1145/1851476.1851552 http://doi.acm.org/10.1145/1851476.1851552 en_US
dc.identifier.uri http://hdl.handle.net/1805/2609
dc.description This thesis examines biomedical text mining with an application in bone biology. A special thanks is extended to Anita Park and Mark Jaeger from the Purdue University Graduate School Office, who acted as invaluable assets in the formatting of the thesis. IUPUI and every other university would be fortunate to have staff that respond in such a timely, corteous, and professional manner. en_US
dc.description Indiana University-Purdue University Indianapolis (IUPUI)
dc.description.abstract The biological literature is a huge and constantly increasing source of information which the biologist may consult for information about their field, but the vast amount of data can sometimes become overwhelming. Medline, which makes a great amount of biological journal data available online, makes the development of automated text mining systems and hence “data-driven discovery” possible. This thesis examines current work in the field of text mining and biological literature, and then aims to mine documents pertaining to bone biology. The documents are retrieved from PubMed, and then direct associations between the terms are computers. Potentially novel transitive associations among biological objects are then discovered using the transitive closure algorithm and the maximum flow algorithm. The thesis discusses in detail the extraction of biological objects from the collected documents and the co-occurrence based text mining algorithm, the transitive closure algorithm, and the maximum network flow which were then run to extract the potentially novel biological associations. Generated hypotheses (novel associations) were assigned with significance scores for further validation by a bone biologist expert. Extension of the work in to hypergraphs for enhanced meaning and accuracy is also examined in the thesis. en_US
dc.language.iso en_US en_US
dc.publisher http://doi.acm.org/10.1145/1851476.1851552 en_US
dc.subject Biomedical text mining en_US
dc.subject Bioinformatics en_US
dc.subject Hypergraphs en_US
dc.subject.lcsh Information storage and retrieval systems -- Biology en_US
dc.subject.lcsh Data mining en_US
dc.subject.lcsh Biological literature en_US
dc.subject.lcsh Hypergraphs en_US
dc.title Biomedical Literature Mining with Transitive Closure and Maximum Network Flow en_US
dc.degree.level M.S. en_US
dc.degree.discipline Computer & Information Science en
dc.degree.grantor Purdue University en_US
dc.degree.date 2011 en_US


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