# Optimizing DNA Sequence Classification via a Deep Learning Hybrid of LSTM and CNN Architecture
## 2 Background and Related Work
This work helped to provide valuable insights into how to optimize machine learning techniques for DNA sequence analysis by demonstrating the benefits and limitations of varying KKK in practical applications. The authors of [19] provided an all-in-one, powerful tool for the speedy and accurate prediction of diseases for DNA sequence classification. The predictors for improving the accuracy of the improvement of disease in DNA sequences include the following methods: support vector machine, convolutional neural network, recurrent neural network, and multi-layer perception. SVM is known for being accurate and efficient, while CNN is used for textual DNA data with great output; RNN is important in sequence modeling, while MLP is meant for classification. The results indicated that the proposed method had the highest accuracy of 93.9% in disease prediction, which is far better compared to other methods. Specifically, CNN achieved 73.5% accuracy, MLP 78.0%, and RNN 69.0%, while on the other hand, the performance of SVM was at a low rate of 50.0% accuracy. Therefore, it highlighted the mistake that the researchers may make when choosing improper machine learning algorithms to predict the disease, since the discovery showed that different algorithms may perform differently on DNA sequence classification. Indeed, the research provided a useful tool for enhancing diagnostic calibration and accuracy in rapid disease prediction using more sophisticated techniques in the field of machine learning. In the experiment by [20], the dataset in the experiment was DNA sequences, and the classification models were assessed based on metrics such as F1 score, recall, precision rate, execution time, and accuracy. The researchers, by incorporating machine learning techniques into pattern-matching algorithms, were efficiently able to search and compare DNA sequences to identify specific query patterns. The study revealed that the proposed model outperformed other algorithms, such as FLPM and PAPM, in terms of accuracy and time complexity. However, it was suggested that further comparison with more algorithms is required to confirm the efficiency of the model. Additionally, the study considered the pattern length affecting the performance of the algorithm, and how important the investigation of algorithm performance with longer or even more complex patterns will be. Recently, there have been developments in deep learning that have introduced transformer-based architectures as robust alternatives to recurrent and convolutional models for DNA sequence classification. Originally developed for natural language processing, the models have turned out to be highly effective in detecting long-range relationships within biological sequences. For instance, DNABERT, a BERT model pre-trained on k-mer representations of genomic data, improved performance in promoter prediction and splice site prediction by applying bidirectional contextual embeddings of DNA fragments [21]. Similarly, genomic BERT also reshapes the transformer model to encode nucleotide interactions and has been applied to a range of genomics tasks with greater accuracy and interpretability than CNNs and RNNs [22]. These transformer models are particularly useful for discovering the rich, non-local patterns in genomic data that are typically discarded in traditional sequence models [23]. Their attention abilities allow the network to dynamically pay attention to important regions within long sequences; therefore, they are especially suitable for high-resolution genomic classification. Meanwhile, graph neural networks (GNNs) have appeared attractive in bioinformatics for their ability to model relational structure and interactions between sequence constituents. Techniques such as graph convolutional networks (GCNs) and graph attention networks (GATs) have been utilized to represent DNA sequences as graphs with k-mers or nucleotides treated as nodes and biological or structural relationships as edges between them.