title: Text and data mining for biomedical discovery.
abstract: The biggest challenge for text and data mining is to truly impact the biomedical discovery process, enabling scientists to generate novel hypothesis to address the most crucial questions. Among a number of worthy submissions, we have selected six papers that exemplify advances in text and data mining methods that have a demonstrated impact on a wide range of applications. Work presented in this session includes data mining techniques applied to the discovery of 3-way genetic interactions and to the analysis of genetic data in the context of electronic medical records (EMRs), as well as an integrative approach that combines data from genetic (SNP) and transcriptomic (microarray) sources for clinical prediction. Text mining advances include a classification method to determine whether a published article contains pharmacological experiments relevant to drug-drug interactions, a fine-grained text mining approach for detecting the catalytic sites in proteins in the biomedical literature, and a method for automatically extending a taxonomy of health-related terms to integrate consumer-friendly synonyms for medical terminologies.
authors: Department of Biomedical Informatics, Arizona State University, Scottsdale, AZ 85259, USA. ggonzalez@asu.edu.
authors: Graciela
authors: G
authors: Gonzalez
authors: Kevin Bretonnel
authors: KB
authors: Cohen
authors: Casey S
authors: CS
authors: Greene
authors: Udo
authors: U
authors: Hahn
authors: Maricel G
authors: MG
authors: Kann
authors: Robert
authors: R
authors: Leaman
authors: Nigam
authors: N
authors: Shah
authors: Jieping
authors: J
authors: Ye
mesh_terms: D019295:Computational Biology / Q000379:methods; D057225:Data Mining / Q000379:methods; D004347:Drug Interactions; D057286:Electronic Health Records; D006801:Humans; D009626:Terminology as Topic