miRTex: A Text Mining System for miRNA-Gene Relation Extraction |
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| Authors: | Gang Li Karen E. Ross Cecilia N. Arighi Yifan Peng Cathy H. Wu K. Vijay-Shanker |
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| Affiliation: | 1. Department of Computer and Information Sciences, University of Delaware, Newark, Delaware, United States of America.; 2. Department of Biochemistry and Molecular & Cellular Biology, Georgetown University Medical Center, Washington, DC, United States of America.; 3. Center for Bioinformatics and Computational Biology, University of Delaware, Newark, Delaware, United States of America.; University of Chicago, UNITED STATES, |
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| Abstract: | 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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