Background MicroRNA (miRNAs) play essential roles in post-transcriptional gene regulation in animals and plants. Precursors of miRNA (pre-miRNA) are characterized by their hairpins structure. However, a large amount of similar sequences can be folded into this kind of structure. Several existing computational approaches have been developed to predict which hairpins can be pre-miRNAs, but they require a sufficient number of known pre-miRNAs and non pre-miRNAs as learning samples. However, most sequenced genomes have a very small number of miRNAs reported and most of the sequences are unlabeled. The semi-supervised approach proposed in this work takes advantage of these sequences to achieve better prediction rates than state-of-theart methods [1].
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