GPU-Enhanced Computational Models for Genetic Data Interpretation

EasyChair Preprint 14035

Abstract

The rapid advancement in genetic research has necessitated the development of computational models capable of efficiently handling and interpreting vast amounts of genetic data. Traditional computational approaches often struggle with the sheer volume and complexity inherent in genomic datasets, leading to the exploration of GPU-accelerated methodologies. This paper investigates the application of GPU-enhanced computational models for genetic data interpretation, highlighting the significant improvements in processing speed and model accuracy. By leveraging the parallel processing capabilities of GPUs, we can dramatically reduce the time required for data analysis, enabling real-time insights and more robust genetic data interpretation. Our study demonstrates the potential of GPU-accelerated models in enhancing various aspects of genetic research, including genome-wide association studies (GWAS), gene expression analysis, and the prediction of protein-protein interactions. The integration of these advanced computational techniques is poised to revolutionize the field of genomics, providing researchers with powerful tools to uncover complex genetic relationships and advance our understanding of genetic diseases. Through comprehensive benchmarking and case studies, we illustrate the transformative impact of GPU-enhanced models on genetic data interpretation, underscoring their critical role in the future of computational biology.

Keyphrases: Genome-Wide Association Studies (GWAS), Single Nucleotide Polymorphisms (SNPs), machine learning

@booklet{EasyChair:14035,
  author    = {Abi Cit},
  title     = {GPU-Enhanced Computational Models for Genetic Data Interpretation},
  howpublished = {EasyChair Preprint 14035},
  year      = {EasyChair, 2024}}