Enhancing Genome-Wide Association Studies with GPU-Accelerated Machine Learning

EasyChair Preprint 13908

Abstract

Genome-Wide Association Studies (GWAS) have revolutionized our understanding of genetic contributions to complex diseases and traits. However, the computational demands of analyzing vast datasets pose significant challenges. Recent advancements in GPU-accelerated machine learning offer promising solutions to expedite GWAS, enhancing both efficiency and scalability. This paper explores the integration of GPU technologies with machine learning algorithms, such as deep learning and ensemble methods, to optimize variant identification and statistical analysis in GWAS. We discuss the potential of GPUs to accelerate key GWAS tasks, including data preprocessing, feature selection, and phenotype prediction, thereby enabling researchers to uncover genetic associations more comprehensively and efficiently. Through case studies and performance evaluations, we highlight the transformative impact of GPU-accelerated approaches in advancing genomic research, paving the way for deeper insights into the genetic basis of human health and disease.

Keyphrases: Accelerated sequence analysis, Bioinformatic algorithms, Computational Proteomics, Computational genomics, Deep learning in bioinformatics, GPU-accelerated machine learning, GPU-based bioinformatics, Genomic data processing, High Performance Computing

@booklet{EasyChair:13908,
  author    = {Abey Litty},
  title     = {Enhancing Genome-Wide Association Studies with GPU-Accelerated Machine Learning},
  howpublished = {EasyChair Preprint 13908},
  year      = {EasyChair, 2024}}