Fast and Efficient Metabolomics Data Analysis Using GPU-Accelerated ML

EasyChair Preprint no. 13993

Abstract

Metabolomics, the comprehensive study of small molecule metabolites within biological systems, plays a pivotal role in understanding cellular processes and disease mechanisms. As the volume and complexity of metabolomics data continue to grow, there is a pressing need for computational tools that can handle large-scale data swiftly and effectively. This abstract explores the integration of GPU-accelerated machine learning (ML) techniques to enhance the speed and efficiency of metabolomics data analysis. By leveraging the parallel processing capabilities of GPUs, this approach aims to significantly reduce computational time while maintaining high accuracy in metabolite identification, quantification, and pathway analysis. Key methodologies such as feature extraction, classification, and regression are optimized using GPU-accelerated algorithms, enabling researchers to uncover biomarkers, metabolic signatures, and intricate metabolic networks with unprecedented efficiency. This abstract underscores the transformative potential of GPU-accelerated ML in advancing metabolomics research, fostering deeper insights into biological systems and accelerating discoveries in personalized medicine and biomarker development.

Keyphrases: Graphics Processing Units, machine learning, Metabolomics Data

@Booklet{EasyChair:13993,
  author = {Abi Cit},
  title = {Fast and Efficient Metabolomics Data Analysis Using GPU-Accelerated ML},
  howpublished = {EasyChair Preprint no. 13993},

  year = {EasyChair, 2024}}