Prediction of Piwi-Interacting RNAs and Their Functions via Convolutional Neural Network

Abstract

In eukaryotic cells, Piwi-interacting RNAs (piRNAs) are the type of short chain non-coding RNA molecules, which interconnect with PIWI proteins. It performs various cellular and genetic functions such as gene-specific protein translation, expression regulation, maintenance, and formulation of germ cells. Seeing the prominent contribution of piRNA in eukaryotic organism cells, many attempts were made to identify it computationally, however, unsatisfactory results were obtained. So, it is requisite to extend the concept of a computational tool in such a way that accurately represents piRNA. In this regard, intelligent and high discriminative deep learning i.e., the convolutional neural network based sequential-computational model known as “piRNA-CNN” is carried out for the prediction of piRNA. RNA sequences are mathematically expressed using the natural language processing method namely: word2vec in order to get prominent, relevant, and high variated numerical descriptors. The proposed “piRNA-CNN” model yields an accuracy of 93.83% for the first-layer in which the provided query RNA molecule is predicted as non-piRNA or piRNA. In case of the piRNA, the proposed model identified the query as mRNA deadenylation or without deadenylation in the second layer, and achieved 91.19% of accuracy. The obtained outcomes authenticated that the piRNA-CNN model exposed substantial results matched to the current tools stated in the literature, so far. It is further expected that the suggested predictive tool will assist scientists and researchers to design improved computational tools.

Keywords

• RNA
• ensemble learning
• genetic algorithm
• word2vec
• CNN