Accelerating Systems Biology Simulations with GPU-Enhanced Machine Learning

EasyChair Preprint 14037

Abstract

Advancements in computational biology have increasingly relied on the integration of machine learning (ML) techniques with high-performance computing technologies like Graphics Processing Units (GPUs) to accelerate complex simulations. This paper explores the application of GPU-enhanced ML methods in accelerating systems biology simulations. By leveraging GPU parallelization, computational tasks such as gene network inference, protein-protein interaction prediction, and microbiome analysis can achieve significant speed-ups, thereby enabling rapid exploration of biological systems at unprecedented scales. This abstract highlights the synergy between GPU acceleration and ML algorithms in pushing the boundaries of systems biology research, offering insights into how these technologies enhance predictive modeling and deepen our understanding of biological processes.

Keyphrases: Central Processing Units (CPUs), Graphics Processing Units (GPUs), Machine Learning (ML)

@booklet{EasyChair:14037,
  author    = {Abi Cit},
  title     = {Accelerating Systems Biology Simulations with GPU-Enhanced Machine Learning},
  howpublished = {EasyChair Preprint 14037},
  year      = {EasyChair, 2024}}