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Predicting miRNA targets utilizing an extended profile HMM

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2010

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Gesellschaft für Informatik e.V.

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The regulation of many cellular processes is influenced by miRNAs, and bioinformatics approaches for predicting miRNA targets evolve rapidly. Here, we propose conditional profile HMMs that learn rules of miRNA-target site interaction automatically from data. We demonstrate that conditional profile HMMs detect the rules implemented into existing approaches from their predictions. And we show that a simple UTR model utilizing conditional profile HMMs predicts target genes of miR- NAs with a precision that is competitive compared to leading approaches, although it does not exploit cross-species conservation.

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Grau, Jan; Arend, Daniel; Grosse, Ivo; Hatzigeorgiou, Artemis G.; Keilwagen, Jens; Maragkakis, Manolis; Weinholdt, Claus; Posch, Stefan (2010): Predicting miRNA targets utilizing an extended profile HMM. German Conference on Bioinformatics 2010. Bonn: Gesellschaft für Informatik e.V.. PISSN: 1617-5468. ISBN: 978-3-88579-267-3. pp. 81-91. Regular Research Papers. Braunschweig. September 20-22, 2010

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