Machine Learning-Driven Pathway Analysis with GPU Acceleration in Bioinformatics

EasyChair Preprint 13902

Abstract

In the realm of bioinformatics, the analysis of biological pathways plays a pivotal role in understanding cellular mechanisms and disease processes. Recent advancements in machine learning (ML) coupled with GPU acceleration have revolutionized pathway analysis by enabling rapid processing of large-scale genomic data. This paper explores the integration of GPU-accelerated ML techniques for pathway analysis, focusing on their capacity to enhance speed and scalability. We discuss methodologies that leverage GPU computing to efficiently handle complex biological datasets, thereby facilitating quicker identification of critical pathways and biomarkers. By harnessing the computational power of GPUs, researchers can uncover novel insights into biological systems with unprecedented efficiency, paving the way for accelerated discoveries in personalized medicine and therapeutic development.

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, Machine learning in computational biology

@booklet{EasyChair:13902,
  author    = {Abey Litty},
  title     = {Machine Learning-Driven Pathway Analysis with GPU Acceleration in Bioinformatics},
  howpublished = {EasyChair Preprint 13902},
  year      = {EasyChair, 2024}}