Identifying proteomic prognostic markers for Alzheimer's disease with survival machine learning: The Framingham Heart Study.

Journal: The journal of prevention of Alzheimer's disease
PMID: 39863332

Abstract

BACKGROUND: Protein abundance levels, sensitive to both physiological changes and external interventions, are useful for assessing the Alzheimer's disease (AD) risk and treatment efficacy. However, identifying proteomic prognostic markers for AD is challenging by their high dimensionality and inherent correlations.

Authors

  • Yuanming Leng
    Department of Biostatistics, Boston University School of Public Health, Boston, MA 02118, USA.
  • Huitong Ding
    Department of Anatomy and Neurobiology, Avedisian School of Medicine, Boston University Chobanian, Boston, MA, United States.
  • Ting Fang Alvin Ang
    Department of Anatomy and Neurobiology, Boston University Chobanian & Avedisian School of Medicine, Boston, MA 02118, USA; Framingham Heart Study, Boston University Chobanian & Avedisian School of Medicine, Boston, MA 02118, USA; Slone Epidemiology Center, Boston University Chobanian & Avedisian School of Medicine, Boston, MA 02118, USA.
  • Rhoda Au
    Boston University School of Medicine, rhodaau@bu.edu.
  • P Murali Doraiswamy
    Department of Psychiatry and Behavioral Sciences, Duke University School of Medicine, United States; Department of Medicine, Duke University School of Medicine, United States.
  • Chunyu Liu
    College of Information Science and Technology, Beijing Normal University, Beijing, China. Electronic address: lcy@mail.bnu.edu.cn.

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