Predicting mirna targets utilizing an extended profile HMM
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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