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Showing 741 - 749 results of 749 for search '(( algorithm within function ) OR ((( algorithm python function ) OR ( algorithm npc function ))))', query time: 0.42s Refine Results
  1. 741
    Enrichment analysis of GO.

    Enrichment analysis of GO. by Yinghao Ren (17915291)

    Published 2025
    “…We also identified differentially expressed genes (DEGs) within the clusters and between SLE patients and healthy controls. …”
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  2. 742
    Lasso gene RF hub gene.

    Lasso gene RF hub gene. by Yinghao Ren (17915291)

    Published 2025
    “…We also identified differentially expressed genes (DEGs) within the clusters and between SLE patients and healthy controls. …”
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  3. 743
    Enrichment analysis of KEGG.

    Enrichment analysis of KEGG. by Yinghao Ren (17915291)

    Published 2025
    “…We also identified differentially expressed genes (DEGs) within the clusters and between SLE patients and healthy controls. …”
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  4. 744
    Image 1_Characterization of cancer-related fibroblasts in bladder cancer and construction of CAFs-based bladder cancer classification: insights from single-cell and multi-omics analysis.tif

    Image 1_Characterization of cancer-related fibroblasts in bladder cancer and construction of CAFs-based bladder cancer classification: insights from single-cell and multi-omics ana... by Zhaokai Zhou (15239078)

    Published 2025
    “…Moreover, machine learning algorithms were applied to identify novel potential targets for each subtype, and experimentally validate their effects.…”
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  5. 745
    Table 1_Characterization of cancer-related fibroblasts in bladder cancer and construction of CAFs-based bladder cancer classification: insights from single-cell and multi-omics analysis.docx

    Table 1_Characterization of cancer-related fibroblasts in bladder cancer and construction of CAFs-based bladder cancer classification: insights from single-cell and multi-omics ana... by Zhaokai Zhou (15239078)

    Published 2025
    “…Moreover, machine learning algorithms were applied to identify novel potential targets for each subtype, and experimentally validate their effects.…”
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  6. 746
    Image 2_Characterization of cancer-related fibroblasts in bladder cancer and construction of CAFs-based bladder cancer classification: insights from single-cell and multi-omics analysis.tif

    Image 2_Characterization of cancer-related fibroblasts in bladder cancer and construction of CAFs-based bladder cancer classification: insights from single-cell and multi-omics ana... by Zhaokai Zhou (15239078)

    Published 2025
    “…Moreover, machine learning algorithms were applied to identify novel potential targets for each subtype, and experimentally validate their effects.…”
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  7. 747
    Raw LC-MS/MS and RNA-Seq Mitochondria data

    Raw LC-MS/MS and RNA-Seq Mitochondria data by Stefano Martellucci (16284377)

    Published 2025
    “…Differentially altered pathways were evaluated by using the enrich plot package in R for visualization of functional enrichment (i.e., dot plot).</p>…”
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  8. 748
    DataSheet1_Mitochondrial-related genes as prognostic and metastatic markers in breast cancer: insights from comprehensive analysis and clinical models.docx

    DataSheet1_Mitochondrial-related genes as prognostic and metastatic markers in breast cancer: insights from comprehensive analysis and clinical models.docx by Yutong Fang (16621143)

    Published 2024
    “…Moreover, leveraging the GSE102484 dataset, we conducted differential gene expression analysis to identify MRGs related to metastasis, subsequently developing metastasis models via 10 distinct machine-learning algorithms and then selecting the best-performing model. …”
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  9. 749
    Table1_Mitochondrial-related genes as prognostic and metastatic markers in breast cancer: insights from comprehensive analysis and clinical models.xlsx

    Table1_Mitochondrial-related genes as prognostic and metastatic markers in breast cancer: insights from comprehensive analysis and clinical models.xlsx by Yutong Fang (16621143)

    Published 2024
    “…Moreover, leveraging the GSE102484 dataset, we conducted differential gene expression analysis to identify MRGs related to metastasis, subsequently developing metastasis models via 10 distinct machine-learning algorithms and then selecting the best-performing model. …”
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