GPU-Enhanced Computational Models for Cancer Genomics

EasyChair Preprint 14204

Abstract

The advent of GPU-enhanced computational models has revolutionized the field of cancer genomics, enabling unprecedented speed and accuracy in data processing and analysis. This paper explores the integration of Graphics Processing Units (GPUs) in computational frameworks to enhance the study of cancer genomics. By leveraging the parallel processing capabilities of GPUs, complex genomic data can be analyzed more efficiently, facilitating rapid identification of genetic mutations, biomarkers, and potential therapeutic targets. The research highlights key advancements in GPU-accelerated algorithms for sequence alignment, variant calling, and gene expression analysis, demonstrating significant performance improvements over traditional CPU-based methods. Additionally, the application of deep learning models on GPU platforms offers enhanced predictive power for cancer prognosis and treatment response. This paper underscores the transformative potential of GPU technology in cancer genomics, advocating for its broader adoption to accelerate research and improve patient outcomes. The implications of these advancements suggest a future where real-time genomic analysis becomes a cornerstone of personalized cancer therapy, ultimately contributing to more effective and targeted treatment strategies.

Keyphrases: computational models, genomics, of Graphics Processing Units (GPUs)

@booklet{EasyChair:14204,
  author    = {Abill Robert},
  title     = {GPU-Enhanced Computational Models for Cancer Genomics},
  howpublished = {EasyChair Preprint 14204},
  year      = {EasyChair, 2024}}