# Optimizing DNA Sequence Classification via a Deep Learning Hybrid of LSTM and CNN Architecture
## 2 Background and Related Work  
Another way of classifying DNA sequences was proposed by [7], including some non-parametric techniques combined with the k-nearest-neighbor classification method. The approach makes use of the compression rate of different algorithms such as Gzip, Snappy, Brotil, LZ4, Z standard, BZ2, and LZMA for measuring sequence similarity. Such methodology provides a resource-efficient solution by comparing sequences according to their compressed forms for an accurate classification. It has been found that the Brotil compression algorithm, which is a slow process, achieved the best classification accuracy of DNA sequences in the case of human beings, chimpanzees, and dog species. This again proves the strength of embedding the compression algorithms into the classification processes for both efficiency and precision in applications related to bioinformatics. [PERSON] proposed a new class of classification with the integration of compression algorithms and k-nearest-neighbor algorithms in classification. These results help mitigate the limitation of computational resources to a certain extent at competitive accuracies, paving the way for an improvement in the efficiency and effectiveness of DNA sequence analysis. The highly advanced machine learning-based predictor developed by [8] for DNA i-motifs that detects secondary DNA structures in cytosine-rich DNA sequences. Putative-iM-Searcher is an algorithm that predicts i-motif conformations by using different strategies, such as overlapping and non-overlapping or greedy and non-greedy methods. In more detail, the tool iM-Seeker uses Putative-iM-Searcher to predict regions of the human genome containing putative i-motif-forming sequences. This is followed by the use of the balanced random forest model to predict the status, and an XGBoost regression model is used to target the strength of folding of the same motifs. The balanced random forest model yielded accuracy, recall, specificity, and AUROC scores of 81%, 77%, 81%, and 87%, respectively, for i-motif formation. Overall, this model performed excellently on all folded i-motifs, even the unfolded C-rich sequences. Additionally, very good generalization ability was proven by the five-fold cross-validation, that showed the AUROC scores were always higher than 0.8. Among various checked regression models for the prediction of i-motif folding strength, the best results were obtained using XGBoost (a high value of R\({}^{2}\) = 0.458 and small errors); therefore, it can be stated as very effective for this type of prediction. To improve efficiency in DNA sequence analysis classification using different machine learning methods. Ref. [9] developed, a full model is proposed in which the pattern-matching algorithms are combined with a range of machine-learning classifiers such as random forest, k-nearest neighbors, naive Bayes, decision tree, and support vector machine. This involves the following steps: DNA data preprocessing, feature extraction, development of the classification model, and lastly, pattern matching for the determination of sequence similarity. The authors used metrics like the F1 score, recall, precision, execution time, and accuracy to evaluate the performance of these algorithms. Among all tested classifiers, the SVM linear model had the best accuracy and F1 score in this study; hence, this model performed better in DNA sequence classification tasks. Their results have some useful practical implications for applications in drug discovery, personalized medicine, and disease diagnosis. They also discuss the pros of each algorithm; thus, this study acts like a guideline on how to choose which technique to use for specific jobs or sets of data. Any further research in this direction, therefore, is likely to result in the fine-tuning of the techniques that have so far been proposed and may, in all probability, lead to the increased accuracy and speed of DNA sequence analysis.