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DISCOVERY AND PRIORITIZATION OF BIOLOGICAL ENTITIES UNDERLYING COMPLEX DISORDERS BY PHENOME-GENOME NETWORK INTEGRATION

GUDIVADA, RANGA CHANDRA
http://rave.ohiolink.edu/etdc/view?acc_num=ucin1195161740

Abstract Details

2007, PhD, University of Cincinnati, Engineering : Biomedical Engineering.
An important goal for biomedical research is to elucidate causal and modifier networks of human disease. While integrative functional genomics approaches have shown success in the identification of biological modules associated with normal and disease states, a critical bottleneck is representing knowledge capable of encompassing asserted or derivable causality mechanisms. Both single gene and more complex multifactorial diseases often exhibit several phenotypes and a variety of approaches suggest that phenotypic similarity between diseases can be a reflection of shared activities of common biological modules composed of interacting or functionally related genes. Thus, analyzing the overlaps and interrelationships of clinical manifestations of a series of related diseases may provide a window into the complex biological modules that lead to a disease phenotype. In order to evaluate our hypothesis, we are developing a systematic and formal approach to extract phenotypic information present in textual form within Online Mendelian Inheritance in Man (OMIM) and Syndrome DB databases to construct a disease - clinical phenotypic feature matrix to be used by various clustering procedures to find similarity between diseases. Our objective is to demonstrate relationships detectable across a range of disease concept types modeled in UMLS to analyze the detectable clinical overlaps of several Cardiovascular Syndromes (CVS) in OMIM in order to find the associations between phenotypic clusters and the functions of underlying genes and pathways. Most of the current biomedical knowledge is spread across different databases in different formats and mining these datasets leads to large and unmanageable results. Semantic Web principles and standards provide an ideal platform to integrate such heterogeneous information and could allow the detection of implicit relations and the formulation of interesting hypotheses. We implemented a page-ranking algorithm onto Semantic Web to prioritize biological entities for their relative contribution and relevance which can be combined with this clustering approach. In this way, disease-gene, disease-pathway or disease-process relationships could be prioritized by mining a phenome - genome framework that not only discovers but also determines the importance of the resources by making queries of higher order relationships of multi-dimensional data that reflect the feature complexity of diseases.
Dr. Bruce Aronow (Advisor)
145 p.
Semantic Web; RDF; OWL; SPARQL; Ontology; Biomedical Informatics; Bioinformatics; Integrative Bioinformatics; Text Mining; Phenome; Genome; Disease Modularity; Data Integration; Semantic Integration

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Citations

  • GUDIVADA, R. C. (2007). DISCOVERY AND PRIORITIZATION OF BIOLOGICAL ENTITIES UNDERLYING COMPLEX DISORDERS BY PHENOME-GENOME NETWORK INTEGRATION [Doctoral dissertation, University of Cincinnati]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1195161740

    APA Style (7th edition)

  • GUDIVADA, RANGA CHANDRA. DISCOVERY AND PRIORITIZATION OF BIOLOGICAL ENTITIES UNDERLYING COMPLEX DISORDERS BY PHENOME-GENOME NETWORK INTEGRATION. 2007. University of Cincinnati, Doctoral dissertation. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=ucin1195161740.

    MLA Style (8th edition)

  • GUDIVADA, RANGA CHANDRA. "DISCOVERY AND PRIORITIZATION OF BIOLOGICAL ENTITIES UNDERLYING COMPLEX DISORDERS BY PHENOME-GENOME NETWORK INTEGRATION." Doctoral dissertation, University of Cincinnati, 2007. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1195161740

    Chicago Manual of Style (17th edition)

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© 2007, all rights reserved.
This open access ETD is published by University of Cincinnati and OhioLINK.