Real-Time Bioinformatics Data Integration Using GPU-Accelerated Machine Learning

EasyChair Preprint 14232

Abstract

The integration of bioinformatics data in real-time is pivotal for advancing precision medicine and enabling dynamic insights into biological processes. Traditional methods for data integration often struggle with the massive scale and complexity of bioinformatics datasets. This paper explores the potential of GPU-accelerated machine learning to address these challenges by significantly enhancing the speed and efficiency of data integration. We present a novel framework that leverages the parallel processing capabilities of GPUs to perform real-time integration of diverse bioinformatics datasets, including genomic, proteomic, and metabolomic data. Our results demonstrate that GPU acceleration can reduce data processing times by up to 80% compared to conventional CPU-based approaches, while maintaining high accuracy and reliability. The proposed framework is evaluated through a series of case studies, highlighting its application in real-time disease outbreak prediction, biomarker discovery, and personalized medicine. The findings underscore the transformative potential of GPU-accelerated machine learning in bioinformatics, paving the way for more responsive and adaptive biological research and healthcare solutions.

Keyphrases: Bioinformatics, Graphics Processing Units (GPUs), machine learning

@booklet{EasyChair:14232,
  author    = {Abill Robert},
  title     = {Real-Time Bioinformatics Data Integration Using GPU-Accelerated Machine Learning},
  howpublished = {EasyChair Preprint 14232},
  year      = {EasyChair, 2024}}