GPU-Enhanced Predictive Modeling for Host-Pathogen Interactions

EasyChair Preprint 14191

Abstract

The study of host-pathogen interactions is crucial for understanding infectious diseases and developing effective therapeutic strategies. Traditional computational methods often face limitations in handling the complexity and volume of biological data involved. This research explores the use of GPU-enhanced predictive modeling to address these challenges. By leveraging the parallel processing power of Graphics Processing Units (GPUs), we significantly improve the efficiency and accuracy of predictive models designed to analyze host-pathogen interactions. Our approach involves the integration of deep learning techniques with GPU acceleration to analyze large-scale biological datasets, identify critical interaction patterns, and predict pathogen behavior within host organisms. We present a novel framework that combines advanced neural network architectures with GPU optimization strategies to achieve real-time processing capabilities. The results demonstrate substantial performance gains in terms of both computational speed and predictive accuracy, providing deeper insights into the mechanisms of infection and potential therapeutic targets. This GPU-enhanced modeling framework holds promise for advancing our understanding of host-pathogen dynamics and supporting the development of innovative treatment approaches

Keyphrases: Convolutional Neural Networks (CNNs), Genome-Wide Association Studies (GWAS), Graphics Processing Units (GPUs)

@booklet{EasyChair:14191,
  author    = {Abill Robert},
  title     = {GPU-Enhanced Predictive Modeling for Host-Pathogen Interactions},
  howpublished = {EasyChair Preprint 14191},
  year      = {EasyChair, 2024}}