Leveraging GPU Acceleration for Epigenomics Data Analysis with Machine Learning

EasyChair Preprint no. 13910

Abstract

Epigenomics, the study of heritable changes in gene expression that do not involve alterations to the underlying DNA sequence, plays a crucial role in understanding complex biological processes and disease mechanisms. Traditional data analysis methods in epigenomics are computationally intensive, often requiring significant time and resources. This paper explores the potential of leveraging Graphics Processing Unit (GPU) acceleration to enhance the efficiency and performance of epigenomics data analysis using machine learning techniques. By harnessing the parallel processing capabilities of GPUs, we aim to significantly reduce the time required for data processing and model training, enabling real-time analysis and more sophisticated machine learning models. Our approach integrates advanced deep learning algorithms and GPU-optimized libraries to handle large-scale epigenomics datasets, facilitating the identification of epigenetic markers and regulatory elements with greater accuracy and speed. We present case studies demonstrating the application of GPU-accelerated machine learning in various epigenomic analyses, including DNA methylation, histone modification, and chromatin accessibility. The results highlight substantial improvements in computational efficiency and predictive performance, underscoring the transformative potential of GPU acceleration in epigenomics research. This advancement promises to accelerate discoveries in epigenetic regulation and its implications in health and disease, paving the way for more personalized and timely medical interventions.

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

@Booklet{EasyChair:13910,
  author = {Abey Litty},
  title = {Leveraging GPU Acceleration for Epigenomics Data Analysis with Machine Learning},
  howpublished = {EasyChair Preprint no. 13910},

  year = {EasyChair, 2024}}