A MeSH-based text mining method for identifying novel prebiotics.
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 number...
| Publicado en: | Medicine Vol. 95; no. 49; pp. 1 - 10 |
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| Autores principales: | , , , , , , , , , |
| Formato: | research tables/charts Journal Article |
| Publicado: |
Lippincott Williams & Wilkins
12/6/2016
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=120583572&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 120583572 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 00257974 2S6 jtl: Medicine issn: 00257974 maglogo: N pubinfo: dt: 12/6/2016 vid: 95 iid: 49 pid: 433 pub: Lippincott Williams & Wilkins place: Baltimore, Maryland artinfo: ui: 120583572 120583572 NLM27930574 120583572 10.1097/MD.0000000000005585 NLM27930574 120583572 ppf: 1 ppct: 9 formats: tig: atl: A MeSH-based text mining method for identifying novel prebiotics. aug: au: Guangyu Shan Yiming Lu Bo Min Wubin Qu Chenggang Zhang Shan, Guangyu Lu, Yiming Min, Bo Qu, Wubin Zhang, Chenggang affil: Beijing Institute of Radiation Medicine, State Key Laboratory of Proteomics, Cognitive and Mental Health Research Center, Beijing, PR China sug: subj: Probiotics Pharmacodynamics Data Mining Methods Subject Headings Probiotics Therapeutic Use Reproducibility of Results Validation Studies Comparative Studies Evaluation Research Multicenter Studies Human ab: 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. pubtype: Academic Journal doctype: research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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