Accelerating Protein-Protein Interaction Network Analysis with GPU and ML

EasyChair Preprint no. 14034

Abstract

Protein-protein interaction (PPI) networks play a crucial role in understanding biological processes and disease mechanisms. Analyzing these networks often involves computationally intensive tasks that benefit from parallel processing technologies like Graphics Processing Units (GPUs) and machine learning (ML) algorithms. This paper explores the acceleration of PPI network analysis using GPU-accelerated ML models. By leveraging the parallel computing power of GPUs, coupled with the efficiency of ML algorithms, this study aims to enhance the scalability and speed of PPI network inference and analysis. We discuss the application of deep learning techniques for feature extraction and classification within PPI networks, demonstrating significant improvements in computational efficiency and predictive accuracy. Case studies highlight the efficacy of GPU-accelerated ML approaches in unraveling complex interactions within biological systems, offering new insights into disease pathways and therapeutic targets. This research underscores the transformative potential of GPU-accelerated ML in advancing biomedical research and precision medicine applications.

Keyphrases: Graphics Processing Units, network analysis, Protein-protein interaction (PPI)

@Booklet{EasyChair:14034,
  author = {Abi Cit},
  title = {Accelerating Protein-Protein Interaction Network Analysis with GPU and ML},
  howpublished = {EasyChair Preprint no. 14034},

  year = {EasyChair, 2024}}