Grau, JanArend, DanielGrosse, IvoHatzigeorgiou, Artemis G.Keilwagen, JensMaragkakis, ManolisWeinholdt, ClausPosch, StefanSchomburg, DietmarGrote, Andreas2019-01-172019-01-172010978-3-88579-267-3https://dl.gi.de/handle/20.500.12116/19675The 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.enText/Conference Paper1617-5468