# Optimizing DNA Sequence Classification via a Deep Learning Hybrid of LSTM and CNN Architecture
## 2 Background and Related Work
In this paper, this algorithm was applied to three DNA sequences, which were chimpanzee, dog, and human datasets, to classify the gene family and to find the original vs. mutant sequences. It investigated the effect of the substring length parameter, k value, on the classification performance, showing that increasing the k value improves the metrics of accuracy, precision, recall, and F1 score up to an optimal point. Beyond this threshold, performance declines due to increased computational overhead. The research demonstrated that the multinomial naive Bayes algorithm achieved a notable accuracy of 98.4%, outperforming other classifiers in both accuracy and flexibility with varying gene sequence lengths. This study highlighted the algorithm's efficacy in DNA sequence classification and its valuable contribution to bioinformatics, particularly in managing diverse and complex genetic data. In the work by [14], the derivative Boyer-Moore (d-BM) was established to escalate computational efficiency when handling compressed DNA sequences. This method effectively narrows down DNA sequences and sequences, particularly the ones that are elongated. This review paper looked at how Bayesian networks can be applied in complex tasks of precise DNA identification for forensic purposes. For example, in forensics, the models are useful when working with samples that have minimal DNA quantities and smears. In the same year, another study was proposed by [15] for a faster version of Boyer-Moore (d-BM) to speed up the computation on compressed DNA sequences. This approach narrows down DNA sequences as well as sequences, especially ones that elongate. Furthermore, the research work conducted by [16] proposed context-sensitive methods for the purpose of identifying RNA structures inside the secondary structures. The system interfaces with the language of RNA Spec using a user interface developed in Java. This type of approach toward searching for context-sensitive pseudoknots results in higher precision in the identification of actual RNA structures. The work in [17] presents a machine learning-based approach for identifying enhancer regions of DNA. A random forest classifier was employed for building the model. It handled the high complexity of this dataset very effectively and ensured accurate predictions. This research investigated the application of statistical moments as features in a better discriminative model that classified enhancer versus non-enhancer regions with good classification performance. The research demonstrated that the random forest classifier achieved an accuracy of 91.68% for enhancer classification and 84.53% for strong enhancer classification, based on five-fold cross-validation with a benchmark dataset. This method not only surpassed existing techniques in accuracy but also highlighted the importance of precise enhancer prediction for understanding gene expression regulation. The results highlight the potential of the model to contribute significantly to the field by providing a strong computational tool for enhancer identification and assessment of their functional role. The research proposed by [18] described a DNA sequence classification approach using the multinomial naive Bayes algorithm. Their proposed method includes DNA sequencing, K-mer counting, thresholding, and sequence analysis to identify whether gene sequences are normal or abnormal. The research paper evaluated various classifier performances by tuning the substring length parameter, KKK, and observed that, with an increase in KKK, accuracy, precision, recall, and F1 score initially improved. However, beyond an optimal threshold, further increases in KKK resulted in diminished performance due to the increased overhead of managing a larger number of substrings. The findings underscored the effectiveness of the multinomial naive Bayes algorithm in classifying gene sequences and pointed to the impact of substring length on classifier performance.