title: Extraction of genotype-phenotype-drug relationships from text: from entity recognition to bioinformatics application.
abstract: Advances in concept recognition and natural language parsing have led to the development of various tools that enable the identification of biomedical entities and relationships between them in text. The aim of the Genotype-Phenotype-Drug Relationship Extraction from Text workshop (or GPD-Rx workshop) is to examine the current state of art and discuss the next steps for making the extraction of relationships between biomedical entities integral to the curation and knowledge management workflow in Pharmacogenomics. The workshop will focus particularly on the extraction of Genotype-Phenotype, Genotype-Drug, and Phenotype-Drug relationships that are of interest to Pharmacogenomics. Extracting and structuring such text-mined relationships is a key to support the evaluation and the validation of multiple hypotheses that emerge from high throughput translational studies spanning multiple measurement modalities. In order to advance this agenda, it is essential that existing relationship extraction methods be compared to one another and that a community wide benchmark corpus emerges; against which future methods can be compared. The workshop aims to bring together researchers working on the automatic or semi-automatic extraction of relationships between biomedical entities from research literature in order to identify the key groups interested in creating such a benchmark.
authors.affiliation: Department of Genetics, Stanford University, Stanford, CA 94305, USA. coulet@stanford.edu
authors.forename: Adrien
authors.initials: A
authors.lastname: Coulet
authors.forename: Nigam
authors.initials: N
authors.lastname: Shah
authors.forename: Lawrence
authors.initials: L
authors.lastname: Hunter
authors.forename: Chitta
authors.initials: C
authors.lastname: Barral
authors.forename: Russ B
authors.initials: RB
authors.lastname: Altman
mesh_terms: D019295:Computational Biology; D057225:Data Mining; D056726:Genetic Association Studies; D006801:Humans; D009323:Natural Language Processing; D010597:Pharmacogenetics / Q000592:standards / Q000706:statistics & numerical data*