Motivations

We love to call ourselves a bit advanced Human Being.  Deoxyribonucleic acid(DNA) which transfer the genetic information from one generation to next generation. DNA sequencing has revolutionized the field of biology, but it is still a complex and expensive process. Deep learning has the potential to make DNA sequencing more accurate, affordable, and accessible. Deep learning models can also be used to analyze DNA sequence data in new and innovative ways, leading to new discoveries about human health and biology. 

The motivation behind the integration of deep learning into DNA sequence analysis lies in its ability to decipher the non-linear relationships and intricate dependencies within biological data. Traditional methods often struggle to capture the nuances present in vast genomic datasets. Deep learning algorithms, with their capacity to learn hierarchical representations, offer a promising avenue to comprehend the underlying complexities of DNA sequences. By decoding these complexities, researchers can gain insights into genetic variations, gene expression regulation, and disease mechanisms, paving the way for targeted therapies and improved healthcare outcomes.