Leveraging GPU Acceleration for Metagenomics Data Analysis Using Machine Learning

EasyChair Preprint 13779

Abstract

Recent advancements in metagenomics have revolutionized our understanding of microbial communities, presenting vast opportunities and challenges in data analysis. This study explores the integration of GPU acceleration with machine learning techniques to enhance the efficiency and scalability of metagenomics data analysis. By leveraging the parallel processing power of GPUs, coupled with advanced algorithms, this research aims to optimize tasks such as sequence alignment, feature extraction, and classification within metagenomic datasets. Through comparative analysis and performance metrics, the study demonstrates significant improvements in computational speed and throughput, thereby enabling more rapid and accurate insights into microbial diversity, functional potential, and ecological dynamics. The findings underscore the transformative impact of GPU-accelerated machine learning in advancing metagenomics research and its potential applications in diverse fields including environmental microbiology, biotechnology, and personalized medicine.

Keyphrases: Metagenomic dataset, Microbiology, machine learning

@booklet{EasyChair:13779,
  author    = {Abill Robert},
  title     = {Leveraging GPU Acceleration for Metagenomics Data Analysis Using Machine Learning},
  howpublished = {EasyChair Preprint 13779},
  year      = {EasyChair, 2024}}