Deep Learning for Biomarker Discovery: Performance Gains with GPU Acceleration

EasyChair Preprint 13840

Abstract

Biomarker discovery is pivotal in advancing personalized medicine, offering potential for early disease detection, prognosis, and tailored treatments. Recent advancements in deep learning have revolutionized this field, providing powerful tools for analyzing complex biological data. However, the computational demands of deep learning algorithms pose significant challenges. This paper explores the integration of GPU acceleration to enhance the performance of deep learning models in biomarker discovery. We delve into the architecture of GPU-accelerated deep learning frameworks, highlighting their capability to process large-scale genomic and proteomic datasets efficiently. Our findings demonstrate substantial improvements in training times, model accuracy, and overall computational efficiency. Additionally, we discuss case studies where GPU-accelerated deep learning models have successfully identified novel biomarkers for diseases such as cancer and neurodegenerative disorders. The implications of these advancements suggest a promising future for biomarker discovery, enabling faster, more accurate identification of disease markers and fostering the development of precision medicine. This paper underscores the transformative potential of combining deep learning with GPU acceleration, setting a new benchmark in biomedical research.

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:13840,
  author    = {Abey Litty},
  title     = {Deep Learning for Biomarker Discovery: Performance Gains with GPU Acceleration},
  howpublished = {EasyChair Preprint 13840},
  year      = {EasyChair, 2024}}