Predicting novel microRNA: a comprehensive comparison of machine learning approaches

Abstract

This review provides a comprehensive study and comparative assessment of methods from these two machine learning (ML) approaches for dealing with the prediction of novel pre-miRNAs: supervised and unsupervised training.We present and analyze the machine learning proposals that have appeared during the last 10 years in literature. They have been compared in several prediction tasks involving two model genomes and increasing imbalance levels. This work provides a review of existing ML approaches for premiRNAprediction and fair comparisons of the classifiers with same features and data sets, instead of just a revision of published software tools. The results and the discussion can help the community to select the most adequate bioinformatics approach according to the prediction task at hand. The comparative results obtained suggest that from low to mid imbalance levels between classes, supervised methods can be the best. However, at very high imbalance levels, closer to real case scenarios, models including unsupervised and deep learning can provide better performance.