Accelerating Proteomics Data Analysis with GPU and Machine Learning

EasyChair Preprint 14187

Abstract

Proteomics data analysis, an essential component of biological research and personalized medicine, involves the comprehensive study of proteomes to understand protein functions, structures, and interactions. The complexity and volume of proteomics data pose significant challenges, requiring advanced computational techniques for efficient processing and analysis. This paper explores the transformative potential of leveraging Graphics Processing Units (GPUs) and machine learning (ML) to accelerate proteomics data analysis. GPUs, known for their parallel processing capabilities, offer substantial improvements in computational speed and efficiency over traditional Central Processing Units (CPUs). By integrating ML algorithms with GPU acceleration, we aim to enhance various stages of proteomics data analysis, including protein identification, quantification, and post-translational modification (PTM) detection. This approach not only reduces the computational time but also improves the accuracy and sensitivity of proteomic analyses. We demonstrate the efficacy of GPU-accelerated ML models through case studies and performance benchmarks, highlighting the potential for real-time data processing and analysis. The findings suggest that the adoption of GPU-accelerated ML techniques can significantly advance proteomics research, enabling more rapid and precise insights into protein dynamics and facilitating breakthroughs in biomedical research and therapeutic development.

Keyphrases: Central Processing Units (CPUs), data analysis, machine learning

@booklet{EasyChair:14187,
  author    = {Abill Robert},
  title     = {Accelerating Proteomics Data Analysis with GPU and Machine Learning},
  howpublished = {EasyChair Preprint 14187},
  year      = {EasyChair, 2024}}