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A MeSH-based text mining method for identifying novel prebiotics.
Shan, Guangyu; Lu, Yiming; Min, Bo; Qu, Wubin; Zhang, Chenggang.
Afiliação
  • Shan G; Beijing Institute of Radiation Medicine, State Key Laboratory of Proteomics, Cognitive and Mental Health Research Center, Beijing, PR China.
Medicine (Baltimore) ; 95(49): e5585, 2016 Dec.
Article em En | MEDLINE | ID: mdl-27930574
ABSTRACT
Prebiotics contribute to the well-being of their host by altering the composition of the gut microbiota. Discovering new prebiotics is a challenging and arduous task due to strict inclusion criteria; thus, highly limited numbers of prebiotic candidates have been identified. Notably, the large numbers of published studies may contain substantial information attached to various features of known prebiotics that can be used to predict new candidates. In this paper, we propose a medical subject headings (MeSH)-based text mining method for identifying new prebiotics with structured texts obtained from PubMed. We defined an optimal feature set for prebiotics prediction using a systematic feature-ranking algorithm with which a variety of carbohydrates can be accurately classified into different clusters in accordance with their chemical and biological attributes. The optimal feature set was used to separate positive prebiotics from other carbohydrates, and a cross-validation procedure was employed to assess the prediction accuracy of the model. Our method achieved a specificity of 0.876 and a sensitivity of 0.838. Finally, we identified a high-confidence list of candidates of prebiotics that are strongly supported by the literature. Our study demonstrates that text mining from high-volume biomedical literature is a promising approach in searching for new prebiotics.
Assuntos

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Probióticos / Medical Subject Headings / Mineração de Dados Tipo de estudo: Observational_studies / Prognostic_studies Idioma: En Revista: Medicine (Baltimore) Ano de publicação: 2016 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Probióticos / Medical Subject Headings / Mineração de Dados Tipo de estudo: Observational_studies / Prognostic_studies Idioma: En Revista: Medicine (Baltimore) Ano de publicação: 2016 Tipo de documento: Article