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Showing 1 - 7 results of 7 for search 'multi-algorithm agent learning', query time: 0.12s Refine Results
  1. 1
    Image 4_Integrated machine learning analysis of 30 cell death patterns identifies a novel prognostic signature in glioma.jpeg

    Image 4_Integrated machine learning analysis of 30 cell death patterns identifies a novel prognostic signature in glioma.jpeg by Minhao Huang (4952764)

    Published 2025
    “…A pan-death prognostic signature (Cell-Death Score, CDS), constructed via multi-algorithm machine learning and optimized using CoxBoost to incorporate 25 key genes, demonstrated robust performance in training (1-/3-year AUC = 0.894/0.943) and validation cohort (C-index = 0.717), effectively stratifying high-risk patients (HR = 3.21, p < 0.0001). …”
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  2. 2
    Table 2_Integrated machine learning analysis of 30 cell death patterns identifies a novel prognostic signature in glioma.xlsx

    Table 2_Integrated machine learning analysis of 30 cell death patterns identifies a novel prognostic signature in glioma.xlsx by Minhao Huang (4952764)

    Published 2025
    “…A pan-death prognostic signature (Cell-Death Score, CDS), constructed via multi-algorithm machine learning and optimized using CoxBoost to incorporate 25 key genes, demonstrated robust performance in training (1-/3-year AUC = 0.894/0.943) and validation cohort (C-index = 0.717), effectively stratifying high-risk patients (HR = 3.21, p < 0.0001). …”
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  3. 3
    Table 1_Integrated machine learning analysis of 30 cell death patterns identifies a novel prognostic signature in glioma.xlsx

    Table 1_Integrated machine learning analysis of 30 cell death patterns identifies a novel prognostic signature in glioma.xlsx by Minhao Huang (4952764)

    Published 2025
    “…A pan-death prognostic signature (Cell-Death Score, CDS), constructed via multi-algorithm machine learning and optimized using CoxBoost to incorporate 25 key genes, demonstrated robust performance in training (1-/3-year AUC = 0.894/0.943) and validation cohort (C-index = 0.717), effectively stratifying high-risk patients (HR = 3.21, p < 0.0001). …”
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  4. 4
    Image 3_Integrated machine learning analysis of 30 cell death patterns identifies a novel prognostic signature in glioma.jpeg

    Image 3_Integrated machine learning analysis of 30 cell death patterns identifies a novel prognostic signature in glioma.jpeg by Minhao Huang (4952764)

    Published 2025
    “…A pan-death prognostic signature (Cell-Death Score, CDS), constructed via multi-algorithm machine learning and optimized using CoxBoost to incorporate 25 key genes, demonstrated robust performance in training (1-/3-year AUC = 0.894/0.943) and validation cohort (C-index = 0.717), effectively stratifying high-risk patients (HR = 3.21, p < 0.0001). …”
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  5. 5
    Image 2_Integrated machine learning analysis of 30 cell death patterns identifies a novel prognostic signature in glioma.jpeg

    Image 2_Integrated machine learning analysis of 30 cell death patterns identifies a novel prognostic signature in glioma.jpeg by Minhao Huang (4952764)

    Published 2025
    “…A pan-death prognostic signature (Cell-Death Score, CDS), constructed via multi-algorithm machine learning and optimized using CoxBoost to incorporate 25 key genes, demonstrated robust performance in training (1-/3-year AUC = 0.894/0.943) and validation cohort (C-index = 0.717), effectively stratifying high-risk patients (HR = 3.21, p < 0.0001). …”
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  6. 6
    Table 3_Integrated machine learning analysis of 30 cell death patterns identifies a novel prognostic signature in glioma.xlsx

    Table 3_Integrated machine learning analysis of 30 cell death patterns identifies a novel prognostic signature in glioma.xlsx by Minhao Huang (4952764)

    Published 2025
    “…A pan-death prognostic signature (Cell-Death Score, CDS), constructed via multi-algorithm machine learning and optimized using CoxBoost to incorporate 25 key genes, demonstrated robust performance in training (1-/3-year AUC = 0.894/0.943) and validation cohort (C-index = 0.717), effectively stratifying high-risk patients (HR = 3.21, p < 0.0001). …”
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  7. 7
    Image 1_Integrated machine learning analysis of 30 cell death patterns identifies a novel prognostic signature in glioma.jpeg

    Image 1_Integrated machine learning analysis of 30 cell death patterns identifies a novel prognostic signature in glioma.jpeg by Minhao Huang (4952764)

    Published 2025
    “…A pan-death prognostic signature (Cell-Death Score, CDS), constructed via multi-algorithm machine learning and optimized using CoxBoost to incorporate 25 key genes, demonstrated robust performance in training (1-/3-year AUC = 0.894/0.943) and validation cohort (C-index = 0.717), effectively stratifying high-risk patients (HR = 3.21, p < 0.0001). …”
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