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dc.contributor.authorTarkhadkar, Sagar Santosh
dc.date.accessioned2014-07-08T04:30:16Z
dc.date.available2014-07-08T04:30:16Z
dc.date.issued2013-12
dc.identifier.othertarkhadkar_sagar_s_201312_ms
dc.identifier.urihttp://purl.galileo.usg.edu/uga_etd/tarkhadkar_sagar_s_201312_ms
dc.identifier.urihttp://hdl.handle.net/10724/30031
dc.description.abstractManual curation of knowledge from biomedical literature is both expensive and time consuming. Scientific publications in biomedicine have an enormous amount of valuable information on gene mutations and their impacts, which is significant in addressing multiple research problems. In this thesis, we have developed a text mining system for extracting and curating mutation impacts from full text scientific documents. The objective of this system is to populate biomedical knowledge-bases with accurate knowledge regarding mutation impacts, in a semi-automated way. We have used a number of Natural Language Processing tasks in developing this system. Furthermore, a curation module allows the scientists to decide if the mutation impact information is suitable to be included to the knowledge base, hence eliminating the possibility of adding incorrect data. Our prototype system has been used in the Protein Kinase domain, but can be adapted to work in other domains, in the future.
dc.languageeng
dc.publisheruga
dc.rightspublic
dc.subjecttext mining
dc.subjectNatural Language Processing
dc.titleMutaimpact miner
dc.title.alternativea gene-mutation impact extractor and curator for biomedical literature
dc.typeThesis
dc.description.degreeMS
dc.description.departmentComputer Science
dc.description.majorComputer Science
dc.description.advisorKrzysztof J. Kochut
dc.description.committeeKrzysztof J. Kochut
dc.description.committeeKhaled Rasheed
dc.description.committeeIsmailcem Budak Arpinar


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