# Optimizing DNA Sequence Classification via a Deep Learning Hybrid of LSTM and CNN Architecture
## 2 Background and Related Work
The machine learning methods for bioinformatics that are strictly related to multiple aspects of analyzing DNA sequencing data strands and several major fields of genomics were reviewed by [10]. This work demonstrated how the algorithms associated with machine learning are used for the organization of large-scale data obtained with the help of the contemporary sequencing tool. In particular, the review targeted the application of ML in genome assembly, gene annotation, variant calling, and GWAS. These techniques are useful for lifting short DNA reads towards the correct positions for assembling genomes, discovering gene and functional regions, distinguishing between genetic differences and sequencing errors, and identifying genetic markers associated with particular traits or diseases. Because of the complexity of genomics, the inclusion of machine learning has brought desirable improvements in genomic research. With these algorithms, the features of the datasets have been modeled, predictions on what might be the underlying link between genetics and diseases have been made, and the advancement of drug targeting and customized medicine has been enhanced. It thereby establishes that machine learning has changed the face of genomics and shows how it is able to browse through heaps of available genomic data and generate useful knowledge and information that would otherwise have gone unnoticed by normal methods. A comprehensive review of the applications of machine learning to DNA sequence data mining was conducted by [11], focusing on classification, clustering, and pattern recognition methods. The study investigated how these methods help in extracting meaningful information from vast datasets and discovering significant patterns in the sequences of DNA. Importantly, the paper highlighted the pros and cons of various machine learning algorithms in relation to their capability for filtering through a vast volume of data for patterns that might not be noticed using other methods. It emphasized the importance of predictable patterns found in biological systems by utilizing machine learning as a tool in predicting DNA sequence functions and understanding evolutionary relationships. This article discussed the evolution of sequencing technology, challenges relating to the analysis of DNA sequence data, and possible machine learning approaches that may solve them. In fact, through these advanced algorithms, a deeper understanding of genetic information can be acquired by researchers about biological systems, thereby affirming that it would be impossible without machine learning techniques advancing DNA sequence data analysis. It is insightful to the readers, as it provides an understanding of how the application of machine learning tools would improve interpretation. The use of advanced machine learning and deep learning techniques to enhance DNA sequencing was highlighted by [12]. This paper incorporated the decision trees, random forests, and other types of deep learning models to improve DNA data analysis. These approaches are vital in predicting potential medical conditions based on genetic information and have been one of the ways in which Industry 4.0 technologies have found application in medicine for more precise diagnoses and better outcomes. The paper exposed how the sophisticated methods are of some help for the reasonable categorization of DNA sequences and forecasts of health status for patients. Such sophisticated algorithms allow the researcher to demonstrate the strength that machine learning and deep learning models can achieve while yielding key insights into genetic data to improve the accuracy of a prediction, which provides the best practices in patient care. Thus, it presented the transformative potential of these technologies in medical diagnoses by highlighting their role in the advancement of DNA sequencing methods. The investigation into DNA sequence classification based on machine learning was conducted by [13], with a focus was especially around the multinomial naive Bayes algorithm.