Enhancing Cancer Genomics Research with GPU-Accelerated Machine Learning Techniques

EasyChair Preprint 13759

Abstract

Cancer genomics research, with its complexity and vast datasets, demands advanced computational techniques to uncover meaningful insights and drive personalized medicine. This paper explores the integration of GPU-accelerated machine learning techniques to enhance cancer genomics research. The study highlights how GPUs, with their parallel processing capabilities, significantly expedite the analysis of large-scale genomic data, enabling more efficient and accurate identification of genetic mutations and biomarkers. By leveraging machine learning algorithms, researchers can better predict cancer susceptibility, treatment responses, and disease progression. This approach not only accelerates the data processing pipeline but also improves the precision of predictive models, ultimately contributing to more tailored and effective therapeutic strategies. The paper also addresses the challenges of implementing GPU-accelerated machine learning in cancer genomics, including data heterogeneity, algorithm optimization, and the need for interdisciplinary collaboration. Through a series of case studies and performance benchmarks, we demonstrate the transformative potential of these technologies in advancing cancer research and paving the way for breakthroughs in oncology.

Keyphrases: Cancer genomics research, Graphics Processing Units (GPUs), Machine Learning Algorithms

@booklet{EasyChair:13759,
  author    = {Abill Robert},
  title     = {Enhancing Cancer Genomics Research with GPU-Accelerated Machine Learning Techniques},
  howpublished = {EasyChair Preprint 13759},
  year      = {EasyChair, 2024}}