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  • Title: Identifying disease-causal genes using Semantic Web-based representation of integrated genomic and phenomic knowledge.
    Author: Gudivada RC, Qu XA, Chen J, Jegga AG, Neumann EK, Aronow BJ.
    Journal: J Biomed Inform; 2008 Oct; 41(5):717-29. PubMed ID: 18755295.
    Abstract:
    Most common chronic diseases are caused by the interactions of multiple factors including the influences and responses of susceptibility and modifier genes that are themselves subject to etiologic events, interactions, and environmental factors. These entities, interactions, mechanisms, and phenotypic consequences can be richly represented using graph networks with semantically definable nodes and edges. To use this form of knowledge representation for inferring causal relationships, it is critical to leverage pertinent prior knowledge so as to facilitate ranking and probabilistic treatment of candidate etiologic factors. For example, genomic studies using linkage analyses detect quantitative trait loci that encompass a large number of disease candidate genes. Similarly, transcriptomic studies using differential gene expression profiling generate hundreds of potential disease candidate genes that themselves may not include genetically variant genes that are responsible for the expression pattern signature. Hypothesizing that the majority of disease-causal genes are linked to biochemical properties that are shared by other genes known to play functionally important roles and whose mutations produce clinical features similar to the disease under study, we reasoned that an integrative genomics-phenomics approach could expedite disease candidate gene identification and prioritization. To approach the problem of inferring likely causality roles, we generated Semantic Web methods-based network data structures and performed centrality analyses to rank genes according to model-driven semantic relationships. Our results indicate that Semantic Web approaches enable systematic leveraging of implicit relations hitherto embedded among large knowledge bases and can greatly facilitate identification of centrality elements that can lead to specific hypotheses and new insights.
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