TY - JOUR AU - Marques, Yuri Bento AU - de Paiva Oliveira, Alcione AU - Ribeiro Vasconcelos, Ana Tereza AU - Cerqueira, Fabio Ribeiro PY - 2016 DA - 2016/12/15 TI - Mirnacle: machine learning with SMOTE and random forest for improving selectivity in pre-miRNA ab initio prediction JO - BMC Bioinformatics SP - 474 VL - 17 IS - 18 AB - MicroRNAs (miRNAs) are key gene expression regulators in plants and animals. Therefore, miRNAs are involved in several biological processes, making the study of these molecules one of the most relevant topics of molecular biology nowadays. However, characterizing miRNAs in vivo is still a complex task. As a consequence, in silico methods have been developed to predict miRNA loci. A common ab initio strategy to find miRNAs in genomic data is to search for sequences that can fold into the typical hairpin structure of miRNA precursors (pre-miRNAs). The current ab initio approaches, however, have selectivity issues, i.e., a high number of false positives is reported, which can lead to laborious and costly attempts to provide biological validation. This study presents an extension of the ab initio method miRNAFold, with the aim of improving selectivity through machine learning techniques, namely, random forest combined with the SMOTE procedure that copes with imbalance datasets. SN - 1471-2105 UR - https://doi.org/10.1186/s12859-016-1343-8 DO - 10.1186/s12859-016-1343-8 ID - Marques2016 ER -