A Novel Multi-Input Neural Network Model for MicroRNA Target-Site Detection

Abstract Purpose: MicroRNAs are tiny non-coding RNA sequences that regulate gene expressions and play crucial roles in controlling cell activities. They function by binding to their target messenger RNAs (mRNAs) and suppressing protein synthesis. Detecting the binding location of a microRNA is an important step towards discovering microRNA functions. Given the many possibilities that a microRNA can bind to an mRNA, microRNA target-site detection is a challenging task in vivo and in vitro; from time and cost limitations of experimental methods to high false positive rates of computational methods. Methods: In this paper, we propose a multi-input neural network-based algorithm that yields high recall and precision results simultaneously. Moreover, we designed a Dynamic Programming(DP) algorithm to predict the duplex structure of microRNA target-site, specific to the context of these sequences. The predicted duplex structures, substructures resulting from the DP algorithm, minimum free energy (MFE) of the substructures, and a probabilistic image of possible base pairs in the duplex are all fed in parallel into our algorithm to learn a comprehensive and precise model. Results and Conclusions: Our method on an experimentally validated test set, detects target-sites with AUPRC of 0.9373, Precision of 0.8725, Recall of 0.8703, and outperforms several commonly used computational methods of microRNA target predictions. In addition, using the duplex structure, MFE, and binding probabilities, rather than the nucleotide sequences of the microRNA and targetsites, enables our model to generalize beyond specific sequence contexts and perform well on sequentially distant samples. The source code for our algorithm and the accompanying datasets are freely accessible on Github at: https://github.com/mohebbimg/minn.git