High-dimensional biomarker identification for interpretable disease prediction via machine learning models.

Journal: Bioinformatics (Oxford, England)
Published Date: May 6, 2025

Abstract

MOTIVATION: Omics features, often measured by high-throughput technologies, combined with clinical features, significantly impact the understanding of many complex human diseases. Integrating key omics biomarkers with clinical risk factors is essential for elucidating disease mechanisms, advancing early diagnosis, and enhancing precision medicine. However, the high dimensionality and intricate associations between disease outcomes and omics profiles present substantial analytical challenges.

Authors

  • Yifan Dai
    Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, United States.
  • Di Wu
    University of Melbourne, Melbourne, VIC 3010 Australia.
  • Ian Carroll
    Department of Anesthesiology, Stanford University, Stanford, California, United States of America.
  • Fei Zou
    Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
  • Baiming Zou
    Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.

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