Accelerating Computational Biology Workflows with Machine Learning and GPU

EasyChair Preprint 13906

Abstract

Computational biology has revolutionized biological research by enabling large-scale data analysis and modeling. This paper explores the integration of machine learning techniques with GPU acceleration to enhance computational biology workflows. By leveraging the parallel processing power of GPUs, tasks such as sequence alignment, molecular dynamics simulations, and genomic data analysis can be expedited significantly. Machine learning algorithms further optimize these processes by automating feature extraction, pattern recognition, and predictive modeling. This synergy not only accelerates research timelines but also enhances the accuracy and scalability of biological data analysis. Through case studies and performance benchmarks, this study demonstrates the transformative impact of GPU-accelerated machine learning in advancing computational biology, paving the way for innovative applications in genomics, proteomics, and drug discovery.

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:13906,
  author    = {Abey Litty},
  title     = {Accelerating Computational Biology Workflows with Machine Learning and GPU},
  howpublished = {EasyChair Preprint 13906},
  year      = {EasyChair, 2024}}