Combining multi-species genomic data for microRNA identification using a Naïve Bayes classifier

Abstract

Our study shows that the application of machine learning techniques, along with the integration of data from multiple species is a useful and general approach for miRNA gene prediction. Based on our experiments, we believe that this new technique is applicable to an extensive range of eukaryotes' genomes. Specific structure and sequence features are first used to identify miRNAs followed by a comparative analysis to decrease the number of false positives (FPs). The resulting algorithm exhibits higher specificity and similar sensitivity compared to currently used algorithms that rely on conserved genomic regions to decrease the rate of FPs.