Accelerating Functional Genomics Research with GPU and Machine Learning

EasyChair Preprint 14180

Abstract

Functional genomics research aims to understand the roles and interactions of genes and their products in biological systems. The advent of high-throughput sequencing technologies has generated vast amounts of functional genomics data, but analyzing this data efficiently remains a significant challenge. Recent advancements in graphics processing units (GPUs) and machine learning (ML) offer promising solutions for accelerating these analyses. GPUs, with their parallel processing capabilities, enable the rapid computation of complex algorithms required for large-scale data processing. Concurrently, ML techniques, including deep learning and ensemble methods, can extract meaningful patterns and insights from high-dimensional data more effectively than traditional approaches. This paper explores the integration of GPU-accelerated ML models in functional genomics, highlighting their potential to enhance data processing speed, improve accuracy in gene function predictions, and enable real-time analyses of genomic datasets. By leveraging these technologies, researchers can gain deeper insights into gene functions, interactions, and their implications in health and disease, ultimately advancing the field of functional genomics.

Keyphrases: Graphics Processing Units (GPUs), genomics, machine learning

@booklet{EasyChair:14180,
  author    = {Abi Litty},
  title     = {Accelerating Functional Genomics Research with GPU and Machine Learning},
  howpublished = {EasyChair Preprint 14180},
  year      = {EasyChair, 2024}}