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 1.The user types a disease or biological process to be searched. 2.Genes found to be in association to the disease are extracted from the literature. 3.Protein-protein interactions involving those genes are then pulled from the literature & curated sources 4.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. 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 abstractswww.cbioc.org 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.