Quantifying and filtering knowledge generated by literature based discovery.
BMC Bioinformatics
; 18(Suppl 7): 249, 2017 May 31.
Article
em En
| MEDLINE
| ID: mdl-28617217
ABSTRACT
BACKGROUND:
Literature based discovery (LBD) automatically infers missed connections between concepts in literature. It is often assumed that LBD generates more information than can be reasonably examined.METHODS:
We present a detailed analysis of the quantity of hidden knowledge produced by an LBD system and the effect of various filtering approaches upon this. The investigation of filtering combined with single or multi-step linking term chains is carried out on all articles in PubMed.RESULTS:
The evaluation is carried out using both replication of existing discoveries, which provides justification for multi-step linking chain knowledge in specific cases, and using timeslicing, which gives a large scale measure of performance.CONCLUSIONS:
While the quantity of hidden knowledge generated by LBD can be vast, we demonstrate that (a) intelligent filtering can greatly reduce the number of hidden knowledge pairs generated, (b) for a specific term, the number of single step connections can be manageable, andPalavras-chave
Texto completo:
1
Bases de dados:
MEDLINE
Assunto principal:
Mineração de Dados
Limite:
Humans
Idioma:
En
Revista:
BMC Bioinformatics
Assunto da revista:
INFORMATICA MEDICA
Ano de publicação:
2017
Tipo de documento:
Article
País de afiliação:
Reino Unido