Accelerating Microbiome Research with GPU-Accelerated Machine Learning

EasyChair Preprint no. 14002

Abstract

Microbiome research has emerged as a pivotal area in understanding human health and disease, leveraging advancements in sequencing technologies to explore microbial communities' complexity. However, the computational demands of analyzing vast amounts of sequencing data pose significant challenges. This paper explores the integration of GPU-accelerated machine learning techniques to enhance the speed and efficiency of microbiome data analysis. By leveraging the parallel processing power of GPUs, this approach promises to expedite tasks such as taxonomic classification, functional annotation, and biomarker discovery. We discuss specific GPU-accelerated algorithms tailored for microbiome research, highlighting their potential to uncover intricate relationships within microbial ecosystems and facilitate personalized medicine initiatives. This synthesis underscores the transformative impact of GPU technology on advancing microbiome research capabilities, paving the way for deeper insights into microbial influences on human health.

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

@Booklet{EasyChair:14002,
  author = {Abi Cit},
  title = {Accelerating Microbiome Research with GPU-Accelerated Machine Learning},
  howpublished = {EasyChair Preprint no. 14002},

  year = {EasyChair, 2024}}