Paper Title
Study of Vector Representations for DNA SequenceData Classification

Abstract
In the realm of disease-related DNA sequence data, our research endeavors to conduct a comprehensive comparative analysis of diverse methodologies, with a particular focus on exploring the efficacy of vector embeddings tailored for DNA sequences. This multifaceted investigation involves the integration of these embeddings with a state-of-the-art Convolutional Neural Network (CNN), which is subsequently trained on a meticulously curated dataset characterized by its density and reduced-feature dimensions. Notably, our results also highlight a significant reduction in training time, a crucial aspect in the realm of computational biology where rapid analysis of extensive genomic datasets is paramount. This reduction in training time not only enhances the efficiency of the analysis process but also contributes to the scalability of our approach, making it adaptable to large- scale genomic studies. In conclusion, our comparative analysis of various methods, centered around the integration of vector embeddings with CNNs, has provided valuable insights into the potential effectiveness of this approach. By advancing both accuracy and time efficiency in the analysis of extensive genomic datasets, our research contributes to the ongoing efforts in unlocking the full potential of computational techniques for deci- phering the complexities of genomic information. This promising avenue opens new horizons for researchers and practitioners seeking to harness the power of deep learning in the domain of genomics, paving the way for more accurate and expedited disease-related discoveries. Keywords - Genomic Data Analysis, Vector Embeddings, Convolutional Neural Network (CNN)