High-Performance Imaging Genomics with GPU-Enhanced Deep Learning

EasyChair Preprint 14188

Abstract

High-performance imaging genomics has emerged as a transformative approach in understanding complex genetic architectures and their associations with various diseases. The integration of deep learning techniques has significantly advanced the analysis and interpretation of high-dimensional genomic data. However, the computational demands of deep learning algorithms often pose challenges in terms of processing speed and scalability. This study explores the enhancement of imaging genomics through GPU-accelerated deep learning, aiming to achieve unprecedented performance gains in data processing and analysis. By leveraging the parallel processing capabilities of Graphics Processing Units (GPUs), we demonstrate substantial improvements in the efficiency and accuracy of genomic image analysis. The proposed GPU-enhanced deep learning framework facilitates real-time data processing, enabling rapid identification of genomic patterns and biomarkers. Our results highlight the potential of GPU-accelerated methods to revolutionize imaging genomics, providing a robust platform for large-scale genomic studies and precision medicine applications. This research underscores the importance of high-performance computing in advancing genomic sciences and opens new avenues for the integration of AI-driven techniques in biomedical research.

Keyphrases: Graphics Processing Units (GPUs), deep learning, genomics

@booklet{EasyChair:14188,
  author    = {Abill Robert},
  title     = {High-Performance Imaging Genomics with GPU-Enhanced Deep Learning},
  howpublished = {EasyChair Preprint 14188},
  year      = {EasyChair, 2024}}