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dc.contributor.advisorGarner, Harold R.en
dc.creatorWren, Jonathan Danielen
dc.date.accessioned2010-07-12T16:23:35Zen
dc.date.available2010-07-12T16:23:35Zen
dc.date.issued2003-02-01en
dc.identifier.other59006685en
dc.identifier.urihttp://hdl.handle.net/2152.5/230en
dc.description.abstractIndividuals are limited in their ability to read, remember and compare relationships within the vast amount of scientific literature available. This is not only because the amount of literature is increasing exponentially, but the number of things being researched within is as well. Adding to the scale of analysis are new technologies that increase the rate by which data is being gathered from scientific experiments. For most areas of research interest, the scale of analysis exceeds an individual's ability to be aware of all the relationships contained within. Thus, an informatics approach is necessary to identify large-scale trends, shared relationships and novel relationships that are not contained within the literature, but are the logical consequence of the relationships that are. A system has been designed to establish a network of relationships between "objects" of research interest (e.g. genes, chemical compounds, drugs, diseases and clinical phenotypes) by extracting information from scientific text in an automated manner. This system, called IRIDESCENT (Implicit Relationship IDEntification by in-Silico Construction of an Entity-based Network from Text), enables the discovery of novel relationships by identifying and scoring objects sharing large sets of relationships with an object of interest. IRIDESCENT also allows sets of objects to be analyzed for shared relationships, such as responding genes from a microarray experiment. Herein is described the development and workings of IRIDESCENT as well as several well-developed applications of the system.en
dc.format.mediumElectronicen
dc.format.mimetypeapplication/pdfen
dc.language.isoenen
dc.subjectData Miningen
dc.subjectPattern Recognition, Automateden
dc.subjectInformation Storage and Retrievalen
dc.titleThe IRIDESCENT System: An Automated Data-Mining Method to Identify, Evaluate, and Analyze Sets of Relationships Within Textual Databasesen
dc.type.materialTexten
dc.type.genredissertationen
dc.format.digitalOriginborn digitalen
thesis.degree.grantorGraduate School of Biomedical Sciencesen
thesis.degree.departmenten
thesis.degree.nameDoctor of Philosophyen
thesis.degree.levelPh.D.en
thesis.degree.disciplineGenetics and Developmenten
thesis.date.available2004-01-24en


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