Accelerating Epigenetic Data Analysis with GPU-Accelerated Machine Learning

EasyChair Preprint 14079

Abstract

The rapid advancement of next-generation sequencing technologies has generated an immense volume of epigenetic data, presenting both opportunities and challenges for comprehensive analysis. Traditional computational methods often fall short in managing and interpreting this data efficiently due to the sheer scale and complexity. This paper explores the transformative potential of GPU-accelerated machine learning in expediting epigenetic data analysis. By leveraging the parallel processing capabilities of GPUs, machine learning algorithms can significantly enhance the speed and accuracy of identifying epigenetic modifications, such as DNA methylation and histone modifications. We demonstrate the implementation of GPU-accelerated deep learning models in various epigenetic datasets, showcasing substantial improvements in computational efficiency and predictive performance. Our findings highlight the promise of integrating GPU-accelerated machine learning into epigenetic research workflows, paving the way for more rapid and insightful discoveries in the field. This approach not only optimizes data processing pipelines but also facilitates the development of novel biomarkers and therapeutic targets, ultimately contributing to personalized medicine and improved healthcare outcomes.

Keyphrases: Graphics Processing Units (GPUs), Machine Learning (ML), data analysis

@booklet{EasyChair:14079,
  author    = {Abi Cit},
  title     = {Accelerating Epigenetic Data Analysis with GPU-Accelerated Machine Learning},
  howpublished = {EasyChair Preprint 14079},
  year      = {EasyChair, 2024}}