Ranking MEDLINE documents

• Tipo: Artigo em Revista Científica Internacional
• Data: 2014

Autores

Célia Valente (Autor) Outra; Rui Camacho (Autor) FEUP; Eugénio Oliveira (Autor) FEUP

Revista

Título: Journal of the Brazilian Computer Society

Vol. 20 Nº 13

Páginas: 13:1-13:16

ISSN: 0104-6500

Editora: Springer Nature

Indexação

Scopus - 0 Citações

Classificação Científica

FOS: Ciências exactas e naturais > Ciências da computação e da informação

CORDIS: Ciências Físicas > Ciência de computadores > Informática

Outras Informações

ID Authenticus: P-00G-6A1

DOI: 10.1186/1678-4804-20-13

Resumo (PT): Abstract Background BioTextRetriever is a Web-based search tool for retrieving relevant literature in Molecular Biology and related domains from MEDLINE. The core of BioTextRetriever is the dynamic construction of a classifier capable of selecting relevant papers among the whole MEDLINE bibliographic database. “Relevant” papers, in this context, means papers related to a set of DNA or protein sequences provided as input to the tool by the user. Methods Since the number of retrieved papers may be very large, BioTextRetriever uses a novel ranking algorithm to retrieve the most relevant papers first. We have developed a new methodology that enables the automation of the assessment process based on a multi-criteria ranking function. This function combines six factors: MeSH terms, paper’s number of citations, author’s h-index, journals impact factor, author number of publications and journal similarity function. Results The best results highlight the number of citations and the h-index factors. Conclusions We have developed and a multi-criteria ranking function, that contemplates six factors, and that seems appropriate to retrieve relevant papers out of a huge repository such as MEDLINE. Keywords: Ranking; Text mining; Machine learning

Abstract (EN): Background: BioTextRetriever is a Web-based search tool for retrieving relevant literature in Molecular Biology and related domains from MEDLINE. The core of BioTextRetriever is the dynamic construction of a classifier capable of selecting relevant papers among the whole MEDLINE bibliographic database. ¿Relevant¿ papers, in this context, means papers related to a set of DNA or protein sequences provided as input to the tool by the user. Methods: Since the number of retrieved papers may be very large, BioTextRetriever uses a novel ranking algorithm to retrieve the most relevant papers first. We have developed a new methodology that enables the automation of the assessment process based on a multi-criteria ranking function. This function combines six factors: MeSH terms, paper¿s number of citations, author¿s h-index, journals impact factor, author number of publications and journal similarity function. Results: The best results highlight the number of citations and the h-index factors. Conclusions: We have developed and a multi-criteria ranking function, that contemplates six factors, and that seems appropriate to retrieve relevant papers out of a huge repository such as MEDLINE. © 2014, Gonçalves et al.; licensee Springer.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 16