Automatic recognition of topic-classified relations between prostate cancer and genes using MEDLINE abstracts.
BMC Bioinformatics
; 7 Suppl 3: S4, 2006 Nov 24.
Article
em En
| MEDLINE
| ID: mdl-17134477
ABSTRACT
BACKGROUND:
Automatic recognition of relations between a specific disease term and its relevant genes or protein terms is an important practice of bioinformatics. Considering the utility of the results of this approach, we identified prostate cancer and gene terms with the ID tags of public biomedical databases. Moreover, considering that genetics experts will use our results, we classified them based on six topics that can be used to analyze the type of prostate cancers, genes, and their relations.METHODS:
We developed a maximum entropy-based named entity recognizer and a relation recognizer and applied them to a corpus-based approach. We collected prostate cancer-related abstracts from MEDLINE, and constructed an annotated corpus of gene and prostate cancer relations based on six topics by biologists. We used it to train the maximum entropy-based named entity recognizer and relation recognizer.RESULTS:
Topic-classified relation recognition achieved 92.1% precision for the relation (an increase of 11.0% from that obtained in a baseline experiment). For all topics, the precision was between 67.6 and 88.1%.CONCLUSION:
A series of experimental results revealed two importantfindings:
a carefully designed relation recognition system using named entity recognition can improve the performance of relation recognition, and topic-classified relation recognition can be effectively addressed through a corpus-based approach using manual annotation and machine learning techniques.
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Neoplasias da Próstata
/
Processamento de Linguagem Natural
/
Inteligência Artificial
/
Armazenamento e Recuperação da Informação
/
MEDLINE
/
Indexação e Redação de Resumos
/
Proteínas de Neoplasias
Tipo de estudo:
Guideline
/
Prognostic_studies
/
Systematic_reviews
Limite:
Humans
/
Male
Idioma:
En
Ano de publicação:
2006
Tipo de documento:
Article