GPU-Enhanced Deep Learning for High-Throughput Phenotyping in Bioinformatics

EasyChair Preprint 14008

Abstract

High-throughput phenotyping in bioinformatics involves the comprehensive measurement and analysis of phenotypic traits at a large scale, providing crucial insights into biological processes and disease mechanisms. Traditional methods for phenotypic data analysis are often hindered by computational limitations, especially when dealing with large datasets. GPU-enhanced deep learning offers a transformative solution by significantly accelerating the processing and analysis of high-dimensional phenotypic data. This paper explores the application of GPU-accelerated deep learning models in high-throughput phenotyping, emphasizing their ability to handle complex data structures and large-scale datasets with improved efficiency and accuracy. We review recent advancements in GPU technology and deep learning algorithms, demonstrating their impact on phenotypic trait extraction, pattern recognition, and predictive modeling. Additionally, we discuss the integration of GPU-accelerated deep learning with existing bioinformatics pipelines, highlighting case studies that showcase enhanced data throughput and more robust phenotypic insights. Our findings underscore the potential of GPU-enhanced deep learning to revolutionize high-throughput phenotyping, paving the way for more precise and comprehensive understanding of biological systems.

Keyphrases: Bioinformatics, Graphics Processing Units, deep learning

@booklet{EasyChair:14008,
  author    = {Abi Cit},
  title     = {GPU-Enhanced Deep Learning for High-Throughput Phenotyping in Bioinformatics},
  howpublished = {EasyChair Preprint 14008},
  year      = {EasyChair, 2024}}