Machine learning-based identification of an oxidative phosphorylation signature for prognosis, immune infiltration, and drug sensitivity in ovarian cancer - Top-Institutions - MDSpire

Machine learning-based identification of an oxidative phosphorylation signature for prognosis, immune infiltration, and drug sensitivity in ovarian cancer

  • By

  • Luyao Kang

  • Zuchen Yang

  • Yanna Ding

  • Ying Wu

  • Caixia Ma

  • Yaping Wang

  • Canyu Li

  • Bilan Li

  • Gaili Ji

  • May 29, 2026

  • 0 min

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Top Institutions in Oncology

Brief introduction explaining scope and methodology.

  • #1

    The University of Texas MD Anderson Cancer Center
    The University of Texas MD Anderson Cancer Center

    Houston, Texas

    Key Differentiators

    • Oncology

  • #2

    The Cancer Genome Atlas
    The Cancer Genome Atlas

    N/A, N/A

    Key Differentiators

    • Genomics
    • Oncology

  • #3

    Gene Expression Omnibus
    Gene Expression Omnibus

    N/A, N/A

    Key Differentiators

    • Genomics
    • Bioinformatics

  • #4

    Mayo Clinic
    Mayo Clinic

    Rochester, Minnesota

    Key Differentiators

    • Oncology

  • #5

    Gustave Roussy
    Gustave Roussy

    Villejuif, France

    Key Differentiators

    • Oncology

Original Source(s)

Machine learning-based identification of an oxidative phosphorylation signature for prognosis, immune infiltration, and drug sensitivity in ovarian cancer

  • by Luyao Kang, Zuchen Yang, Yanna Ding, Ying Wu, Caixia Ma, Yaping Wang, Canyu Li, Bilan Li, Gaili Ji

  • May 29, 2026

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