title: Text mining for the biocuration workflow.
abstract: Molecular biology has become heavily dependent on biological knowledge encoded in expert curated biological databases. As the volume of biological literature increases, biocurators need help in keeping up with the literature; (semi-) automated aids for biocuration would seem to be an ideal application for natural language processing and text mining. However, to date, there have been few documented successes for improving biocuration throughput using text mining. Our initial investigations took place for the workshop on 'Text Mining for the BioCuration Workflow' at the third International Biocuration Conference (Berlin, 2009). We interviewed biocurators to obtain workflows from eight biological databases. This initial study revealed high-level commonalities, including (i) selection of documents for curation; (ii) indexing of documents with biologically relevant entities (e.g. genes); and (iii) detailed curation of specific relations (e.g. interactions); however, the detailed workflows also showed many variabilities. Following the workshop, we conducted a survey of biocurators. The survey identified biocurator priorities, including the handling of full text indexed with biological entities and support for the identification and prioritization of documents for curation. It also indicated that two-thirds of the biocuration teams had experimented with text mining and almost half were using text mining at that time. Analysis of our interviews and survey provide a set of requirements for the integration of text mining into the biocuration workflow. These can guide the identification of common needs across curated databases and encourage joint experimentation involving biocurators, text mining developers and the larger biomedical research community.
authors.affiliation: The MITRE Corporation, 202 Burlington Road, Bedford, MA 01730, USA. lynette@mitre.org
authors.forename: Lynette
authors.initials: L
authors.lastname: Hirschman
authors.forename: Gully A P C
authors.initials: GA
authors.lastname: Burns
authors.forename: Martin
authors.initials: M
authors.lastname: Krallinger
authors.forename: Cecilia
authors.initials: C
authors.lastname: Arighi
authors.forename: K Bretonnel
authors.initials: KB
authors.lastname: Cohen
authors.forename: Alfonso
authors.initials: A
authors.lastname: Valencia
authors.forename: Cathy H
authors.initials: CH
authors.lastname: Wu
authors.forename: Andrew
authors.initials: A
authors.lastname: Chatr-Aryamontri
authors.forename: Karen G
authors.initials: KG
authors.lastname: Dowell
authors.forename: Eva
authors.initials: E
authors.lastname: Huala
authors.forename: Anália
authors.initials: A
authors.lastname: Lourenço
authors.forename: Robert
authors.initials: R
authors.lastname: Nash
authors.forename: Anne-Lise
authors.initials: AL
authors.lastname: Veuthey
authors.forename: Thomas
authors.initials: T
authors.lastname: Wiegers
authors.forename: Andrew G
authors.initials: AG
authors.lastname: Winter
mesh_terms: D000818:Animals; D035843:Biomedical Research; D057225:Data Mining; D016208:Databases, Factual; D006801:Humans; D009323:Natural Language Processing; D057188:Workflow