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Showing 1 - 5 results of 5 for search 'multiple renal dysfunction algorithm', query time: 0.12s Refine Results
  1. 1
    Prediction to belong to Cluster 1 or Cluster 2 using random forest algorithm.

    Prediction to belong to Cluster 1 or Cluster 2 using random forest algorithm. by Olivier Mascle (14164997)

    Published 2024
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  2. 2
    Data Sheet 1_Association between occupational heat exposure and early renal dysfunction among Chinese petrochemical workers: a combined machine learning and WQS modeling study.docx

    Data Sheet 1_Association between occupational heat exposure and early renal dysfunction among Chinese petrochemical workers: a combined machine learning and WQS modeling study.docx by Qingyu Li (417678)

    Published 2025
    “…</p>Conclusion<p>Occupational heat exposure in petrochemical settings is significantly associated with hyperuricemia, suggesting potential early renal dysfunction risk. Integrating machine learning–based predictive models into workplace health surveillance may facilitate the early identification and management of high-risk workers. …”
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  3. 3
    Table 1_Integrative multi-omics analysis identifies a PTM-related immune signature and IRF9 as a driver in ccRCC.docx

    Table 1_Integrative multi-omics analysis identifies a PTM-related immune signature and IRF9 as a driver in ccRCC.docx by Zixiang Li (7014416)

    Published 2025
    “…</p>Methods<p>We intersected immune-related genes, PTM-related genes, and differentially expressed genes in TCGA-KIRC to derive candidates and built a prognostic model across TCGA and E-MTAB-1980 using multiple algorithms, selecting a random survival forest-based post-translational modification-related signature (PTMRS) with the best performance. …”
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  4. 4
    Supplementary file 1_Integrative multi-omics analysis identifies a PTM-related immune signature and IRF9 as a driver in ccRCC.docx

    Supplementary file 1_Integrative multi-omics analysis identifies a PTM-related immune signature and IRF9 as a driver in ccRCC.docx by Zixiang Li (7014416)

    Published 2025
    “…</p>Methods<p>We intersected immune-related genes, PTM-related genes, and differentially expressed genes in TCGA-KIRC to derive candidates and built a prognostic model across TCGA and E-MTAB-1980 using multiple algorithms, selecting a random survival forest-based post-translational modification-related signature (PTMRS) with the best performance. …”
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  5. 5
    Table 2_Integrative multi-omics analysis identifies a PTM-related immune signature and IRF9 as a driver in ccRCC.docx

    Table 2_Integrative multi-omics analysis identifies a PTM-related immune signature and IRF9 as a driver in ccRCC.docx by Zixiang Li (7014416)

    Published 2025
    “…</p>Methods<p>We intersected immune-related genes, PTM-related genes, and differentially expressed genes in TCGA-KIRC to derive candidates and built a prognostic model across TCGA and E-MTAB-1980 using multiple algorithms, selecting a random survival forest-based post-translational modification-related signature (PTMRS) with the best performance. …”
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