Real-Time Image Processing in Bioinformatics Using GPU-Accelerated Deep Learning

EasyChair Preprint 13907

Abstract

Real-time image processing in bioinformatics has emerged as a critical component in advancing our understanding of complex biological systems. This paper explores the application of GPU-accelerated deep learning techniques to enhance the efficiency and accuracy of image analysis in bioinformatics. Traditional CPU-based image processing methods often fall short in handling the massive datasets and computational complexity inherent in bioinformatics. By leveraging the parallel processing capabilities of GPUs, we demonstrate significant improvements in processing speed and performance.

Our study focuses on key bioinformatics applications, including genomic imaging, cell phenotyping, and disease pathology, where deep learning models have been optimized for GPU acceleration. We present a comprehensive evaluation of various deep learning architectures, such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs), highlighting their effectiveness in real-time image analysis tasks.

Furthermore, we discuss the integration of GPU-accelerated deep learning with existing bioinformatics pipelines, showcasing case studies where these technologies have led to groundbreaking discoveries and enhanced diagnostic accuracy. Our findings indicate that GPU acceleration not only reduces processing time from hours to seconds but also improves the precision and reliability of image-based bioinformatics analyses.

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

@booklet{EasyChair:13907,
  author    = {Abey Litty},
  title     = {Real-Time Image Processing in Bioinformatics Using GPU-Accelerated Deep Learning},
  howpublished = {EasyChair Preprint 13907},
  year      = {EasyChair, 2024}}