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Offers a large collection of literature derived miRNA-gene associations. miRSel combines text-mining results with existing databases and computational predictions. Text mining enables the reliable extraction of microRNA, gene and protein occurrences as well as their relationships from texts. Comprehensive collections of miRNA-gene associations are important for the development of miRNA target prediction tools and the analysis of regulatory networks. miRSel is updated daily and can be queried using a web-based interface via microRNA identifiers, gene and protein names, PubMed queries as well as gene ontology (GO) terms.

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  • Ralf Zimmer <ralf.zimmer at bio.ifi.lmu.de>


Institut für Informatik, Ludwig-Maximilians-Universität München, Amalienstr. 17 80333 München, Germany

Funding source(s)

The German Federal Ministry of Education and Research (FKZ 01GS0801); the Deutscher Akademischer Austausch Dienst (Referat 442, Code A/07/96865)

  • (Naeem et al., 2010) miRSel: automated extraction of associations between microRNAs and genes from the biomedical literature. BMC bioinformatics.
    PMID: 20233441
  • (Akhtar et al., 2016) Bioinformatic tools for microRNA dissection. Nucleic acids research.
    PMID: 26578605

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