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miRTex: A Text Mining System for miRNA-Gene Relation Extraction.
Li, Gang; Ross, Karen E; Arighi, Cecilia N; Peng, Yifan; Wu, Cathy H; Vijay-Shanker, K.
Afiliación
  • Li G; Department of Computer and Information Sciences, University of Delaware, Newark, Delaware, United States of America.
  • Ross KE; Department of Biochemistry and Molecular & Cellular Biology, Georgetown University Medical Center, Washington, DC, United States of America; Center for Bioinformatics and Computational Biology, University of Delaware, Newark, Delaware, United States of America.
  • Arighi CN; Center for Bioinformatics and Computational Biology, University of Delaware, Newark, Delaware, United States of America.
  • Peng Y; Department of Computer and Information Sciences, University of Delaware, Newark, Delaware, United States of America.
  • Wu CH; Department of Computer and Information Sciences, University of Delaware, Newark, Delaware, United States of America; Department of Biochemistry and Molecular & Cellular Biology, Georgetown University Medical Center, Washington, DC, United States of America; Center for Bioinformatics and Computat
  • Vijay-Shanker K; Department of Computer and Information Sciences, University of Delaware, Newark, Delaware, United States of America.
PLoS Comput Biol ; 11(9): e1004391, 2015.
Article en En | MEDLINE | ID: mdl-26407127
MicroRNAs (miRNAs) regulate a wide range of cellular and developmental processes through gene expression suppression or mRNA degradation. Experimentally validated miRNA gene targets are often reported in the literature. In this paper, we describe miRTex, a text mining system that extracts miRNA-target relations, as well as miRNA-gene and gene-miRNA regulation relations. The system achieves good precision and recall when evaluated on a literature corpus of 150 abstracts with F-scores close to 0.90 on the three different types of relations. We conducted full-scale text mining using miRTex to process all the Medline abstracts and all the full-length articles in the PubMed Central Open Access Subset. The results for all the Medline abstracts are stored in a database for interactive query and file download via the website at http://proteininformationresource.org/mirtex. Using miRTex, we identified genes potentially regulated by miRNAs in Triple Negative Breast Cancer, as well as miRNA-gene relations that, in conjunction with kinase-substrate relations, regulate the response to abiotic stress in Arabidopsis thaliana. These two use cases demonstrate the usefulness of miRTex text mining in the analysis of miRNA-regulated biological processes.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Biología Computacional / MicroARNs / Minería de Datos / Genes Tipo de estudio: Prognostic_studies Límite: Humans Idioma: En Revista: PLoS Comput Biol Asunto de la revista: BIOLOGIA / INFORMATICA MEDICA Año: 2015 Tipo del documento: Article País de afiliación: Estados Unidos Pais de publicación: Estados Unidos
Texto completo
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XML
PubMed Links
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Biología Computacional / MicroARNs / Minería de Datos / Genes Tipo de estudio: Prognostic_studies Límite: Humans Idioma: En Revista: PLoS Comput Biol Asunto de la revista: BIOLOGIA / INFORMATICA MEDICA Año: 2015 Tipo del documento: Article País de afiliación: Estados Unidos Pais de publicación: Estados Unidos