Speeding up Gene Expression Data Analysis Using GPU and Machine Learning

EasyChair Preprint 13881

Abstract

Gene expression data analysis plays a crucial role in understanding biological processes and diseases. However, the increasing volume and complexity of genomic data pose significant computational challenges. This paper explores the application of GPU-accelerated machine learning techniques to enhance the speed and efficiency of gene expression data analysis. By leveraging the parallel processing capabilities of GPUs, combined with advanced machine learning algorithms, this research aims to expedite tasks such as feature selection, classification, and clustering in genomic studies. The study evaluates the performance gains achieved through GPU acceleration, comparing them with traditional CPU-based methods. Results demonstrate substantial improvements in computational efficiency, highlighting the potential of GPU-accelerated approaches to revolutionize genomic research and accelerate discoveries in molecular biology and medicine.

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:13881,
  author    = {Abey Litty},
  title     = {Speeding up Gene Expression Data Analysis Using GPU and Machine Learning},
  howpublished = {EasyChair Preprint 13881},
  year      = {EasyChair, 2024}}