title: Annotating the biomedical literature for the human variome.
abstract: This article introduces the Variome Annotation Schema, a schema that aims to capture the core concepts and relations relevant to cataloguing and interpreting human genetic variation and its relationship to disease, as described in the published literature. The schema was inspired by the needs of the database curators of the International Society for Gastrointestinal Hereditary Tumours (InSiGHT) database, but is intended to have application to genetic variation information in a range of diseases. The schema has been applied to a small corpus of full text journal publications on the subject of inherited colorectal cancer. We show that the inter-annotator agreement on annotation of this corpus ranges from 0.78 to 0.95 F-score across different entity types when exact matching is measured, and improves to a minimum F-score of 0.87 when boundary matching is relaxed. Relations show more variability in agreement, but several are reliable, with the highest, cohort-has-size, reaching 0.90 F-score. We also explore the relevance of the schema to the InSiGHT database curation process. The schema and the corpus represent an important new resource for the development of text mining solutions that address relationships among patient cohorts, disease and genetic variation, and therefore, we also discuss the role text mining might play in the curation of information related to the human variome. The corpus is available at http://opennicta.com/home/health/variome.
authors.affiliation: National ICT Australia (NICTA), Victoria Research Laboratory, Level 2, Building 193, The University of Melbourne, Parkville VIC 3010, Australia. karin.verspoor@nicta.com.au
authors.forename: Karin
authors.initials: K
authors.lastname: Verspoor
authors.forename: Antonio
authors.initials: A
authors.lastname: Jimeno Yepes
authors.forename: Lawrence
authors.initials: L
authors.lastname: Cavedon
authors.forename: Tara
authors.initials: T
authors.lastname: McIntosh
authors.forename: Asha
authors.initials: A
authors.lastname: Herten-Crabb
authors.forename: Zoë
authors.initials: Z
authors.lastname: Thomas
authors.forename: John-Paul
authors.initials: JP
authors.lastname: Plazzer
mesh_terms: D057225:Data Mining / Q000379:methods*; D030541:Databases, Genetic; D004194:Disease / Q000235:genetics*; D014644:Genetic Variation; D006801:Humans; D011642:Publications; D012660:Semantics; D013223:Statistics as Topic