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.
Highlights
Families of viruses are grouped supported by their style of nucleic acid as genetic material, DNA, or RNA
E first dataset of Newcastle Disease Virus (NDV) consists of Eighty-three DNA (RNA reverse transcription) sequences that were obtained from different birds in China over the course of 2005; samples were taken from ill or dead poultry. e data were collected and presented in [21]
Each amino acid is predicted in the virus sequence, and it was demonstrated that the amino acids in the sequence influence adjacent amino acid mutations in the sequence using a LSTM deep neural network technique in order to predict new strains
Summary
Families of viruses are grouped supported by their style of nucleic acid as genetic material, DNA, or RNA. DNA viruses usually contain double-stranded DNA (dsDNA) and infrequently single-stranded DNA (ssDNA). Viruses can replicate using DNA-dependent DNA polymerase. RNA viruses have typically ssRNA and contain dsRNA. Ere are two groups of ssRNA viruses, and they are positive-sense (ssRNA (+)) and negative-sense (ssRNA (−)). E genetic material of ssRNA (+) viruses is like mRNA and might be directly translated by the host cell. SsRNA (–) viruses carry RNA complementary to mRNA and must be changed to positive RNA using RNA polymerase at some point in time. DNA strands used this arrangement to generate RNA in a process known as translation. Unlike DNA, RNA is regularly found as a single strand. A few of the viruses are DNA-based, whereas others are RNA-based such as Newcastle, HIV, and flu [2]
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