Mirnacle: machine learning with SMOTE and random forest for improving selectivity in pre-miRNA ab initio prediction

BMC Bioinformatics

Research

Fabio Ribeiro Cerqueira¹  Alcione de Paiva Oliveira²  Yuri Bento Marques³  Ana Tereza Ribeiro Vasconcelos⁴ 

[1] Department of Informatics, Universidade Federal de Viçosa, 36570-900, Viçosa, Brazil;Department of Informatics, Universidade Federal de Viçosa, 36570-900, Viçosa, Brazil;Department of Computer Science, University of Sheffield, Western Bank S10 2TNSheffield, UK;Department of Informatics, Universidade Federal de Viçosa, 36570-900, Viçosa, Brazil;Instituto Federal do Norte de Minas, Rua Mocambi, 39800-430, Teófilo Otoni, Brazil;Laboratório Nacional de Computação Científica, Rua Getúlio Vargas 333, 25651-071, Petropólis, Brazil;

关键词: Pre-miRNA ab initio prediction;    Random forest;    Smote;    microRNA;    Machine learning;    Data mining;   

DOI  :  10.1186/s12859-016-1343-8

来源: Springer

【 摘 要 】

BackgroundMicroRNAs (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.ResultsBy comparing our method, termed Mirnacle, with other important approaches in the literature, we demonstrate that Mirnacle substantially improves selectivity without compromising sensitivity. For the three datasets used in our experiments, our method achieved at least 97% of sensitivity and could deliver a two-fold, 20-fold, and 6-fold increase in selectivity, respectively, compared with the best results of current computational tools.ConclusionsThe extension of miRNAFold by the introduction of machine learning techniques, significantly increases selectivity in pre-miRNA ab initio prediction, which optimally contributes to advanced studies on miRNAs, as the need of biological validations is diminished. Hopefully, new research, such as studies of severe diseases caused by miRNA malfunction, will benefit from the proposed computational tool.

【 授权许可】

CC BY   
© The Author(s) 2016