Overview of the BioCreative VI text-mining services for Kinome Curation Track.
Database (Oxford)
; 20182018 01 01.
Article
en En
| MEDLINE
| ID: mdl-30329035
ABSTRACT
The text-mining services for kinome curation track, part of BioCreative VI, proposed a competition to assess the effectiveness of text mining to perform literature triage. The track has exploited an unpublished curated data set from the neXtProt database. This data set contained comprehensive annotations for 300 human protein kinases. For a given protein and a given curation axis [diseases or gene ontology (GO) biological processes], participants' systems had to identify and rank relevant articles in a collection of 5.2 M MEDLINE citations (task 1) or 530 000 full-text articles (task 2). Explored strategies comprised named-entity recognition and machine-learning frameworks. For that latter approach, participants developed methods to derive a set of negative instances, as the databases typically do not store articles that were judged as irrelevant by curators. The supervised approaches proposed by the participating groups achieved significant improvements compared to the baseline established in a previous study and compared to a basic PubMed search.
Texto completo:
1
Colección:
01-internacional
Banco de datos:
MEDLINE
Asunto principal:
Proteínas Quinasas
/
Minería de Datos
Límite:
Humans
Idioma:
En
Revista:
Database (Oxford)
Año:
2018
Tipo del documento:
Article
País de afiliación:
Suiza