Enhancing DNA Sequence Analysis for Disease Diagnosis and Prediction Using Parallelised Hash-Based Approach

DNA sequencing is a critical tool in genetics, helping to identify disease-causing mutations, predict disease risks, screen for genetic diseases, and monitor disease progression. By determining the order of nucleotides that make up DNA, DNA sequencing allows for the identification of genetic information in specific DNA fragments, entire genomes, or complex micro-biomes. It has transformed disease diagnosis and treatment and has applications in biotechnology, forensic science, and evolutionary biology. Comparing DNA sequences helps researchers identify genetic disorders, cancers, and antibody repertoires, leading to tailored treatments for individual patients. However, the diagnosis of diseases using DNA requires an efficient algorithm due to the large number of nucleotides in DNA sequences. The hash-based approach, an optimal string pattern-matching algorithm, is used to identify relevant patterns in DNA sequences. This approach has shown significant improvement in speed compared to the single pattern matching approach for finding multiple string patterns and a parallelized approach for hash based using GPU is also proposed.