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Text mining biomedical literature for constructing gene regulatory networks

Posted on:2008-08-30Degree:Ph.DType:Dissertation
University:University of FloridaCandidate:Song, YonglingFull Text:PDF
GTID:1440390005965943Subject:Biology
Abstract/Summary:
As the result of decades of active research, tremendous amount of experimental data are available on gene regulatory networks. The ability to dynamically visualize the complex data and logically integrate the knowledge related to gene function, gene regulation, and biomedical evidence would be useful for individual researchers to keep up with all the information and would provide a global view about gene regulatory networks.;In our research, we present the framework of a gene regulatory networks system (GRNS). GRNS automatically mines biomedical literature to extract gene regulatory information (strain number, genotype, gene regulatory relation, and phenotype), automatically constructs gene regulatory networks based on extracted information, and integrates biomedical knowledge into the regulatory networks.;First, GRNS uses an automated text mining technique to extract information about regulatory networks from the collection of biomedical texts. GRNS extracts five kinds of gene regulatory information: strain number, genotype, gene regulatory relation, phenotype, and unrecognized sentence. Based on the extracted gene regulatory information, GRNS can automatically construct and visualize gene regulatory networks. Second, to provide researchers with a clear and global view about the regulatory networks, GRNS uses an interactive visualization method to integrate biomedical evidential information into the regulatory networks. Once a user clicks an entity or a relation of interest, the query interface returns a detailed information page about the clicked entity or relation. GRNS logically integrates the knowledge related to gene function, gene regulation and biomedical evidences, collects genetic evidences, biochemical tests, sequence based predications or biomedical literatures and links this information with regulatory relationships and regulatory entities data. Third, GRNS provides analysis tools for gene regulatory networks. The analysis tools include the frequent graph mining tool and the gene relation predication tool.
Keywords/Search Tags:Gene regulatory, Biomedical, Mining, Gene function gene regulation, GRNS uses, Analysis tools
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