Long Short-Term Memory Neural Networks for RNA Viruses Mutations Prediction

Abstract

Viral progress remains a major deterrent in the viability of antiviral drugs. The ability to anticipate this development will provide assistance in the early detection of drug-resistant strains and may encourage antiviral drugs to be the most effective plan. In recent years, a deep learning model called the seq2seq neural network has emerged and has been widely used in natural language processing. In this research, we borrow this approach for predicting next generation sequences using the seq2seq LSTM neural network while considering these sequences as text data. We used hot single vectors to represent the sequences as input to the model; subsequently, it maintains the basic information position of each nucleotide in the sequences. Two RNA viruses sequence datasets are used to evaluate the proposed model which achieved encouraging results. The achieved results illustrate the potential for utilizing the LSTM neural network for DNA and RNA sequences in solving other sequencing issues in bioinformatics.