GPU-Enhanced Predictive Models for Disease Susceptibility in Computational Biology

EasyChair Preprint no. 14001

Abstract

Recent advancements in computational biology have catalyzed the development of predictive models aimed at understanding disease susceptibility. Leveraging Graphics Processing Units (GPUs) to accelerate these models has emerged as a transformative approach, offering unprecedented computational power and efficiency. This abstract explores the integration of GPU-accelerated machine learning techniques in predicting disease susceptibility, focusing on their application in genomics, proteomics, and metabolomics data analysis. By harnessing GPU capabilities, researchers can expedite large-scale data processing and enhance model complexity, thereby uncovering intricate genetic interactions and biomarkers indicative of disease predisposition. This study underscores the potential of GPU-enhanced predictive models to revolutionize precision medicine, facilitating early detection, personalized treatment strategies, and improved patient outcomes.

Keyphrases: clinical data, Graphics Processing Units, machine learning

@Booklet{EasyChair:14001,
  author = {Abi Cit},
  title = {GPU-Enhanced Predictive Models for Disease Susceptibility in Computational Biology},
  howpublished = {EasyChair Preprint no. 14001},

  year = {EasyChair, 2024}}