Accelerating Functional Annotation of Genomes with GPU and Machine Learning

EasyChair Preprint 14203

Abstract

The functional annotation of genomes is a critical task in genomics, essential for understanding gene function, regulation, and interaction within biological systems. Traditional methods for genome annotation are often time-consuming and computationally intensive due to the vast amounts of data involved. This paper explores the application of Graphics Processing Units (GPUs) and advanced machine learning techniques to accelerate the functional annotation process. Leveraging the parallel processing power of GPUs, coupled with deep learning models, we propose a novel framework that significantly reduces the time required for genome annotation while maintaining high accuracy. Our approach integrates convolutional neural networks (CNNs) and recurrent neural networks (RNNs) to predict gene function, identify regulatory elements, and classify genomic features. Experimental results demonstrate that our GPU-accelerated machine learning framework outperforms traditional CPU-based methods, achieving substantial improvements in processing speed and predictive performance. This advancement not only enhances the efficiency of genomic research but also opens new avenues for real-time analysis and large-scale genomic studies, facilitating faster discoveries in fields such as personalized medicine, evolutionary biology, and biotechnology.

Keyphrases: Genomes, Recurrent Neural Networks (RNNs), machine learning

@booklet{EasyChair:14203,
  author    = {Abill Robert},
  title     = {Accelerating Functional Annotation of Genomes with GPU and Machine Learning},
  howpublished = {EasyChair Preprint 14203},
  year      = {EasyChair, 2024}}