rbpTransformer: A novel deep learning model for prediction of piRNA and mRNA bindings

Abstract

An important issue in biotechnology is predicting whether a piRNA and an mRNA will or will not bind. Research and treatment of diseases, drug discovery, and the silencing and regulation of genes, transposons, and genomic stability may all benefit from accurate binding predictions. The literature offers numerous deep-learning models for piRNA and mRNA binding prediction. However, a proper adjustment of the effective transformer model and the impact of important design alternatives has not been evaluated thoroughly. This paper summarizes the models available in the literature, briefly introduces transformers, then offers a novel deep learning model and evaluates various design alternatives, including k-mer size, number of core modules, choice of optimization algorithm, and whether to use self-attention. The results show that rbpTransformer can be a good candidate for building deep AI models to predict the binding of piRNA and mRNA sequences with an AUC value of 94.38%. The test results also reveal how the design affects the model’s accuracy.