GPU-Enhanced Deep Learning Models for Metabolic Pathway Analysis

EasyChair Preprint 13939

Abstract

Metabolic pathway analysis plays a critical role in understanding the complex biochemical reactions that sustain cellular processes and overall organism health. Traditional methods for analyzing metabolic pathways often face challenges due to the high dimensionality and complexity of biological data. Recent advancements in deep learning have shown significant promise in addressing these challenges, but the computational demands of these models can be prohibitive. This paper explores the integration of Graphics Processing Units (GPUs) to enhance the performance of deep learning models for metabolic pathway analysis. By leveraging the parallel processing capabilities of GPUs, we achieve substantial reductions in training times and improvements in model accuracy. Our GPU-enhanced models facilitate the identification of key metabolic pathways and the prediction of metabolic responses to various stimuli. The findings demonstrate that GPU acceleration not only makes deep learning models more feasible for large-scale metabolic pathway analysis but also unlocks new possibilities for precision medicine and bioengineering. This study underscores the transformative potential of GPU-accelerated deep learning in advancing metabolic research and its applications in health and disease management.

Keyphrases: Accelerated sequence analysis, Bioinformatic algorithms, Computational Proteomics, Computational genomics, Deep learning in bioinformatics, GPU-accelerated machine learning, GPU-based bioinformatics, Genomic data processing, High Performance Computing, Machine learning in computational biology

@booklet{EasyChair:13939,
  author    = {Abi Cit},
  title     = {GPU-Enhanced Deep Learning Models for Metabolic Pathway Analysis},
  howpublished = {EasyChair Preprint 13939},
  year      = {EasyChair, 2024}}