An ensemble SVM model for the accurate prediction of non-canonical MicroRNA targets

Abstract

Background MicroRNAs are small non-coding endogenous RNAs that are responsible for post-transcriptional regulation of genes. Given that large numbers of human genes are targeted by microRNAs, understanding the precise mechanism of microRNA action and accurately mapping their targets is of paramount importance; this will uncover the role of microRNAs in development, differentiation, and disease pathogenesis. However, the current state-of-the-art computational methods for microRNA target prediction suffer from high false-positive rates to be useful in practice. Results In this paper, we develop a suite of models for microRNA target prediction, under the banner Avishkar, that have superior prediction performance over the state-of-the-art protocols. Specifically, our final model developed in this paper achieves an average true positive rate of more than 75%, when keeping the false positive rate of 20%, for non-canonical microRNA target sites in humans. This is an improvement of over 150% in the true positive rate for non-canonical sites, over the best competitive protocol. We are able to achieve such superior performance by representing the thermodynamic and sequence profiles of microRNA-mRNA interaction as curves, coming up with a novel metric of seed enrichment to model seed matches as well as all possible non-canonical matches, and learning an ensemble of microRNA family-specific non-linear SVM classifiers. We provide an easy-to-use system, built on top of Apache Spark, for large-scale interactive analysis and prediction of microRNA targets. All operations in our system, namely candidate set generation, feature generation and transformation, training, prediction and computing performance metrics are fully distributed and are scalable. Availability All source code and sample data is available at https://bitbucket.org/cellsandmachines/avishkar. We also provide scalable implementations of kernel SVM using Apache Spark, which can be used to solve large-scale non-linear binary classification problems at https://bitbucket.org/cellsandmachines/kernelsvmspark.