miRNAss: a semi-supervised approach for microRNA prediction

C. YONES, G. STEGMAYER, D.H. MILONE

Bahia Blanca

Conferencia; VI Argentinian Conference on Bioinformatics and Computational Biology; 2015

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-the-art methods.