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

• 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 [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 Literature: Incorporating Interactions, Connectivity, Confidence, and Context Measures. Pacific Symposium in Biocomputing; 2007; Maui, Hawaii; 2007.Biocomputing; 2007; Maui, Hawaii; 2007.

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

GeneRanker Interface

• Each gene is scored and can be annotated (count of co-occurrences and statistical representation)

CollaborationCollaboration: Application of GeneRanker to : Application of GeneRanker to a biological context, with Dr. Michael Berens, a biological context, with Dr. Michael Berens, Director of the Brain Tumor Unit at the Director of the Brain Tumor Unit at the Translational Genomics Institute (TGen). Translational Genomics Institute (TGen).

GeneRanker is available as an online GeneRanker is available as an online application at application at http://www.generanker.org. .

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)

Mining genes related to glioma: Precision by Method

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

Random List

Gene-disease search

Ranked list (top 200)

Ranked list (top 100)

Ranked list (top 50)

Related (>10 articles) Possibly Related (1 to 10 articles) No evidence of relation or not a gene

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.