GPU-Enhanced Predictive Modeling for Human Microbiome Research

EasyChair Preprint 14052

Abstract

The human microbiome plays a pivotal role in health and disease, with its complex and dynamic nature posing significant challenges for predictive modeling. Recent advancements in computational technologies, particularly Graphics Processing Units (GPUs), offer promising solutions for enhancing predictive modeling in microbiome research. This paper explores the application of GPU-enhanced computational techniques to improve the accuracy and efficiency of predictive models in microbiome studies. By leveraging the parallel processing capabilities of GPUs, researchers can accelerate data analysis, enabling the handling of vast and intricate microbiome datasets more effectively. We review various GPU-accelerated machine learning algorithms and their impact on predicting microbiome-associated health outcomes, microbial interactions, and functional profiles. Additionally, the paper discusses the integration of GPU-based models with high-throughput sequencing technologies to offer deeper insights into microbial community dynamics and their implications for personalized medicine. Through case studies and experimental results, we demonstrate the advantages of GPU-enhanced modeling in identifying biomarkers, understanding microbial mechanisms, and advancing therapeutic interventions. This approach not only improves computational efficiency but also opens new avenues for breakthroughs in microbiome research and its applications in health and disease management.

Keyphrases: Graphics Processing Units, Traditional Computational, microbiome

@booklet{EasyChair:14052,
  author    = {Abi Cit},
  title     = {GPU-Enhanced Predictive Modeling for Human Microbiome Research},
  howpublished = {EasyChair Preprint 14052},
  year      = {EasyChair, 2024}}