Showing 1 - 9 results of 9 for search 'multiple bile selection algorithm', query time: 0.19s Refine Results
  1. 1

    Table 2_Serum metabolomics-based diagnostic biomarkers for colorectal cancer: insights and multi-omics validation.xlsx by Taorui Wang (22300702)

    Published 2025
    “…A metabolomics-based diagnostic model built using ten selected metabolites demonstrated excellent discriminatory performance, achieving area under the receiver operaring characteristic curve (AUROC) of 0.96-0.97 and accuracies up to 92.5% across multiple machine learning methods. …”
  2. 2

    Table 8_Serum metabolomics-based diagnostic biomarkers for colorectal cancer: insights and multi-omics validation.xlsx by Taorui Wang (22300702)

    Published 2025
    “…A metabolomics-based diagnostic model built using ten selected metabolites demonstrated excellent discriminatory performance, achieving area under the receiver operaring characteristic curve (AUROC) of 0.96-0.97 and accuracies up to 92.5% across multiple machine learning methods. …”
  3. 3

    Table 1_Serum metabolomics-based diagnostic biomarkers for colorectal cancer: insights and multi-omics validation.xlsx by Taorui Wang (22300702)

    Published 2025
    “…A metabolomics-based diagnostic model built using ten selected metabolites demonstrated excellent discriminatory performance, achieving area under the receiver operaring characteristic curve (AUROC) of 0.96-0.97 and accuracies up to 92.5% across multiple machine learning methods. …”
  4. 4

    Table 4_Serum metabolomics-based diagnostic biomarkers for colorectal cancer: insights and multi-omics validation.xlsx by Taorui Wang (22300702)

    Published 2025
    “…A metabolomics-based diagnostic model built using ten selected metabolites demonstrated excellent discriminatory performance, achieving area under the receiver operaring characteristic curve (AUROC) of 0.96-0.97 and accuracies up to 92.5% across multiple machine learning methods. …”
  5. 5

    Table 5_Serum metabolomics-based diagnostic biomarkers for colorectal cancer: insights and multi-omics validation.xlsx by Taorui Wang (22300702)

    Published 2025
    “…A metabolomics-based diagnostic model built using ten selected metabolites demonstrated excellent discriminatory performance, achieving area under the receiver operaring characteristic curve (AUROC) of 0.96-0.97 and accuracies up to 92.5% across multiple machine learning methods. …”
  6. 6

    Table 6_Serum metabolomics-based diagnostic biomarkers for colorectal cancer: insights and multi-omics validation.xlsx by Taorui Wang (22300702)

    Published 2025
    “…A metabolomics-based diagnostic model built using ten selected metabolites demonstrated excellent discriminatory performance, achieving area under the receiver operaring characteristic curve (AUROC) of 0.96-0.97 and accuracies up to 92.5% across multiple machine learning methods. …”
  7. 7

    Table 7_Serum metabolomics-based diagnostic biomarkers for colorectal cancer: insights and multi-omics validation.xlsx by Taorui Wang (22300702)

    Published 2025
    “…A metabolomics-based diagnostic model built using ten selected metabolites demonstrated excellent discriminatory performance, achieving area under the receiver operaring characteristic curve (AUROC) of 0.96-0.97 and accuracies up to 92.5% across multiple machine learning methods. …”
  8. 8

    Table 3_Serum metabolomics-based diagnostic biomarkers for colorectal cancer: insights and multi-omics validation.xlsx by Taorui Wang (22300702)

    Published 2025
    “…A metabolomics-based diagnostic model built using ten selected metabolites demonstrated excellent discriminatory performance, achieving area under the receiver operaring characteristic curve (AUROC) of 0.96-0.97 and accuracies up to 92.5% across multiple machine learning methods. …”
  9. 9

    Data Sheet 1_Serum metabolomics-based diagnostic biomarkers for colorectal cancer: insights and multi-omics validation.docx by Taorui Wang (22300702)

    Published 2025
    “…A metabolomics-based diagnostic model built using ten selected metabolites demonstrated excellent discriminatory performance, achieving area under the receiver operaring characteristic curve (AUROC) of 0.96-0.97 and accuracies up to 92.5% across multiple machine learning methods. …”