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GenoScan: Genomic Scanner for Putative miRNA Precursors
University of Skövde, School of Bioscience. University of Skövde, The Systems Biology Research Centre. (Bioinformatics)
University of Skövde, School of Bioscience. University of Skövde, The Systems Biology Research Centre. (Tumor Biology)
University of Skövde, School of Bioscience. University of Skövde, The Systems Biology Research Centre. (Bioinformatics)
2014 (English)In: Bioinformatics Research and Applications: 10th International Symposium, ISBRA 2014, Zhangjiajie, China, June 28-30, 2014. Proceedings / [ed] Mitra Basu, Yi Pan, Jianxin Wang, Springer, 2014, 266-277 p.Conference paper (Refereed)
Abstract [en]

The significance of miRNAs has been clarified over the last decade as thousands of these small non-coding RNAs have been found in a wide variety of species. By binding to specific target mRNAs, miRNAs act as negative regulators of gene expression in many different biological processes. Computational approaches for discovery of miRNAs in genomes usually take the form of an algorithm that scans sequences for miRNA-characteristic hairpins, followed by classification of those hairpins as miRNAs or nonmiRNAs. In this study, two new approaches to genome-scale miRNA discovery are presented and evaluated. These methods, one ensemble-based and one using logistic regression, have been designed to detect miRNA candidates without relying on conservation or transcriptome data, and to achieve high-confidence predictions in reasonable computational time. GenoScan achieves high accuracy with a good balance between sensitivity and specificity. In a benchmark evaluation including 15 previously published methods, the regression-based approach in GenoScan achieved the highest classification accuracy.

Place, publisher, year, edition, pages
Springer, 2014. 266-277 p.
Series
Lecture Notes in Computer Science, ISSN 0302-9743 ; 8492
Keyword [en]
miRNA discovery, machine learning, hairpin classification
National Category
Bioinformatics and Systems Biology
Research subject
Natural sciences
Identifiers
URN: urn:nbn:se:his:diva-10450DOI: 10.1007/978-3-319-08171-7_24ScopusID: 2-s2.0-84906337524ISBN: 978-3-319-08170-0 ISBN: 978-3-319-08171-7 OAI: oai:DiVA.org:his-10450DiVA: diva2:773341
Conference
10th International Symposium on Bioinformatics Research and Applications, ISBRA 2014, Zhangjiajie, China, June 28-30, 2014
Available from: 2014-12-18 Created: 2014-12-18 Last updated: 2015-01-16Bibliographically approved

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CiteExportLink to record
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Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
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More styles
Language
  • de-DE
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Output format
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