Abstract
Deep learning (DL) methods have shown remarkable success in address- ing various problems across different domains. Classifying DNA sequences presents a formidable challenge in the field of bioinformatics. This review delves into various technologies centered around Alignment methods and Deep Learning for the purpose of classification. The aim is to achieve accurate and scalable predictions for DNA sequence classification. DL methods have proven effective in overcoming the primary challenges faced during the train- ing process. The paper delves into previous classification methods like align- ment methods and highlights their limitations. Subsequently, we delve into the application of deep learning, specifically using CNN and RNN models, for DNA sequence classification. We evaluate their respective accuracies and dis- cuss the differences and drawbacks associated with these methods.
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