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Knowledge Integration for Gene Target Selection

Knowledge Integration for Gene Target Selection. Graciela Gonzalez, PhD Juan C. Uribe Contact: graciela.gonzalez@asu.edu. GeneRanker in a Nutshell. Integration of knowledge from biomedical literature curated PPI databases, and protein network topology

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Knowledge Integration for Gene Target Selection

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  1. Knowledge Integration for Gene Target Selection Graciela Gonzalez, PhD Juan C. Uribe Contact: graciela.gonzalez@asu.edu

  2. GeneRanker in a Nutshell • Integration of knowledge from • biomedical literature • curated PPI databases, and • protein network topology • Seeks to prioritize lists of genes on their association to specific diseases and phenotypes [1], • Such associations may or may not have been published (thus, not text mining) [1] Gonzalez G, Uribe JC, Tari L, Brophy C, Baral C. Mining Gene-Disease relationships from Biomedical Literature: Incorporating Interactions, Connectivity, Confidence, and Context Measures. Pacific Symposium in Biocomputing; 2007; Maui, Hawaii; 2007.

  3. GeneRanker Interface • The user types a disease or biological process to be searched. • Genes found to be in association to the disease are extracted from the literature. • Protein-protein interactions involving those genes are then pulled from the literature & curated sources • The protein network is built and each gene ranked

  4. GeneRanker Interface • Each gene is scored and can be annotated (count of co-occurrences and statistical representation) • Collaboration: Application of GeneRanker to a biological context, with Dr. Michael Berens, Director of the Brain Tumor Unit at the Translational Genomics Institute (TGen). • GeneRanker is available as an online application at http://www.generanker.org.

  5. Evaluation of GeneRanker • Contextual (PubMed search) based shows > 20% jump in precision over NLP based extraction. • Synthetic network results show AUC > 0.984 • Empirical validation against a glioma dataset shows consistent results (118 vs 22 differentially expressed probes from top vs bottom of list)

  6. Complementary Work • CBioC: www.cbioc.org shows PPIs, gene-disease, and gene-bioprocess associations extracted from abstracts • BANNER: sourceforge.banner.org (presenting a poster on this one). An open source entity recognizer available now. • Gene normalization: a similar open source system soon to be available.

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