MicroRNAs are involved in many critical cellular activities through binding to their mRNA targets, for example, in cell proliferation, differentiation, death, growth control, and developmental timing. Prediction of microRNA targets can assist in efficient experimental investigations on the functional roles of these small noncoding RNAs. Their accurate prediction, however, remains a challenge due to the limited understanding of underlying processes in recognizing microRNA targets. In this article, we introduce an algorithm that aims at not only predicting microRNA targets accurately but also assisting in vivo experiments to understand the mechanisms of targeting. The algorithm learns a unique hypothesis for each possible mechanism of microRNA targeting. These hypotheses are utilized to build a superior target predictor and for biologically meaningful partitioning of the data set of microRNA-target duplexes. Experimentally verified features for recognizing targets that incorporated in the algorithm enable the establishment of hypotheses that can be correlated with target recognition mechanisms. Our results and analysis show that our algorithm outperforms state-of-the-art data-driven approaches such as deep learning models and machine learning algorithms and rule-based methods for instance miRanda and RNAhybrid. In addition, feature selection on the partitions, provided by our algorithm, confirms that the partitioning mechanism is closely related to biological mechanisms of microRNA targeting. The resulting data partitions can potentially be used for in vivo experiments to aid in the discovery of the targeting mechanisms.
Keywords: data partitioning; machine learning; microRNA; microRNA target prediction; multi-hypotheses learning.