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Showing 1,241 - 1,260 results of 1,330 for search '(( algorithm fibrin function ) OR ((( algorithm python function ) OR ( algorithm b function ))))', query time: 0.40s Refine Results
  1. 1241
    Image 2_Leveraging the integration of bioinformatics and machine learning to uncover common biomarkers and molecular pathways underlying diabetes and nephrolithiasis.tif

    Image 2_Leveraging the integration of bioinformatics and machine learning to uncover common biomarkers and molecular pathways underlying diabetes and nephrolithiasis.tif by Xudong Shen (205653)

    Published 2025
    “…Among 101 machine learning models, S100A4, ARPC1B, and CEBPD were identified as the most significant interacting genes linking diabetes and kidney stones. …”
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  2. 1242
    Image 3_Leveraging the integration of bioinformatics and machine learning to uncover common biomarkers and molecular pathways underlying diabetes and nephrolithiasis.tif

    Image 3_Leveraging the integration of bioinformatics and machine learning to uncover common biomarkers and molecular pathways underlying diabetes and nephrolithiasis.tif by Xudong Shen (205653)

    Published 2025
    “…Among 101 machine learning models, S100A4, ARPC1B, and CEBPD were identified as the most significant interacting genes linking diabetes and kidney stones. …”
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  3. 1243
    Image 1_Leveraging the integration of bioinformatics and machine learning to uncover common biomarkers and molecular pathways underlying diabetes and nephrolithiasis.tif

    Image 1_Leveraging the integration of bioinformatics and machine learning to uncover common biomarkers and molecular pathways underlying diabetes and nephrolithiasis.tif by Xudong Shen (205653)

    Published 2025
    “…Among 101 machine learning models, S100A4, ARPC1B, and CEBPD were identified as the most significant interacting genes linking diabetes and kidney stones. …”
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  4. 1244
    Image 4_Leveraging the integration of bioinformatics and machine learning to uncover common biomarkers and molecular pathways underlying diabetes and nephrolithiasis.tif

    Image 4_Leveraging the integration of bioinformatics and machine learning to uncover common biomarkers and molecular pathways underlying diabetes and nephrolithiasis.tif by Xudong Shen (205653)

    Published 2025
    “…Among 101 machine learning models, S100A4, ARPC1B, and CEBPD were identified as the most significant interacting genes linking diabetes and kidney stones. …”
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  5. 1245
    Table 1_Leveraging the integration of bioinformatics and machine learning to uncover common biomarkers and molecular pathways underlying diabetes and nephrolithiasis.docx

    Table 1_Leveraging the integration of bioinformatics and machine learning to uncover common biomarkers and molecular pathways underlying diabetes and nephrolithiasis.docx by Xudong Shen (205653)

    Published 2025
    “…Among 101 machine learning models, S100A4, ARPC1B, and CEBPD were identified as the most significant interacting genes linking diabetes and kidney stones. …”
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  6. 1246
    Image 5_Leveraging the integration of bioinformatics and machine learning to uncover common biomarkers and molecular pathways underlying diabetes and nephrolithiasis.tif

    Image 5_Leveraging the integration of bioinformatics and machine learning to uncover common biomarkers and molecular pathways underlying diabetes and nephrolithiasis.tif by Xudong Shen (205653)

    Published 2025
    “…Among 101 machine learning models, S100A4, ARPC1B, and CEBPD were identified as the most significant interacting genes linking diabetes and kidney stones. …”
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  7. 1247
    Data Sheet 1_Exploring the molecular mechanisms of phthalates in the comorbidity of preeclampsia and depression by integrating multiple datasets.zip

    Data Sheet 1_Exploring the molecular mechanisms of phthalates in the comorbidity of preeclampsia and depression by integrating multiple datasets.zip by Xinpeng Tian (646275)

    Published 2025
    “…Machine learning algorithms were applied to select core diagnostic genes, followed by validation in independent cohorts. …”
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  8. 1248
    Image 3_Dysregulated arginine metabolism is associated with pro-tumor neutrophil polarization in liver cancer.tif

    Image 3_Dysregulated arginine metabolism is associated with pro-tumor neutrophil polarization in liver cancer.tif by Xingchao Liu (3501161)

    Published 2025
    “…Although neutrophils are recognized as key regulators of LIHC progression, their functional heterogeneity and metabolic drivers are not yet fully understood.…”
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  9. 1249
    Image 5_Dysregulated arginine metabolism is associated with pro-tumor neutrophil polarization in liver cancer.tif

    Image 5_Dysregulated arginine metabolism is associated with pro-tumor neutrophil polarization in liver cancer.tif by Xingchao Liu (3501161)

    Published 2025
    “…Although neutrophils are recognized as key regulators of LIHC progression, their functional heterogeneity and metabolic drivers are not yet fully understood.…”
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  10. 1250
    Image 4_Dysregulated arginine metabolism is associated with pro-tumor neutrophil polarization in liver cancer.tif

    Image 4_Dysregulated arginine metabolism is associated with pro-tumor neutrophil polarization in liver cancer.tif by Xingchao Liu (3501161)

    Published 2025
    “…Although neutrophils are recognized as key regulators of LIHC progression, their functional heterogeneity and metabolic drivers are not yet fully understood.…”
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  11. 1251
    Image 2_Dysregulated arginine metabolism is associated with pro-tumor neutrophil polarization in liver cancer.tif

    Image 2_Dysregulated arginine metabolism is associated with pro-tumor neutrophil polarization in liver cancer.tif by Xingchao Liu (3501161)

    Published 2025
    “…Although neutrophils are recognized as key regulators of LIHC progression, their functional heterogeneity and metabolic drivers are not yet fully understood.…”
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  12. 1252
    Image 1_Dysregulated arginine metabolism is associated with pro-tumor neutrophil polarization in liver cancer.tif

    Image 1_Dysregulated arginine metabolism is associated with pro-tumor neutrophil polarization in liver cancer.tif by Xingchao Liu (3501161)

    Published 2025
    “…Although neutrophils are recognized as key regulators of LIHC progression, their functional heterogeneity and metabolic drivers are not yet fully understood.…”
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  13. 1253
    Image 6_Dysregulated arginine metabolism is associated with pro-tumor neutrophil polarization in liver cancer.tif

    Image 6_Dysregulated arginine metabolism is associated with pro-tumor neutrophil polarization in liver cancer.tif by Xingchao Liu (3501161)

    Published 2025
    “…Although neutrophils are recognized as key regulators of LIHC progression, their functional heterogeneity and metabolic drivers are not yet fully understood.…”
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  14. 1254
    Data Sheet 1_Air pollution-related immune gene prognostic signature for hepatocellular carcinoma: network toxicology, machine learning and multi-omics analysis.pdf

    Data Sheet 1_Air pollution-related immune gene prognostic signature for hepatocellular carcinoma: network toxicology, machine learning and multi-omics analysis.pdf by Lei Pu (608561)

    Published 2025
    “…APIGPS constructed by 7 APIGs (CDC25C, MELK, ATG4B, SLC2A1, CDC25B, APEX1, GLS), demonstrated robust predictive ability independent of clinical features. …”
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  15. 1255
    Table 1_Air pollution-related immune gene prognostic signature for hepatocellular carcinoma: network toxicology, machine learning and multi-omics analysis.xlsx

    Table 1_Air pollution-related immune gene prognostic signature for hepatocellular carcinoma: network toxicology, machine learning and multi-omics analysis.xlsx by Lei Pu (608561)

    Published 2025
    “…APIGPS constructed by 7 APIGs (CDC25C, MELK, ATG4B, SLC2A1, CDC25B, APEX1, GLS), demonstrated robust predictive ability independent of clinical features. …”
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  16. 1256
    Antivirus Engines

    Antivirus Engines by Paul A. Gagniuc (1818325)

    Published 2025
    “…Materialul combină fundamente teoretice cu exemple aplicate, prezentând modele, algoritmi și structuri de date utilizate în detecția amenințărilor informatice, oferind o imagine completă asupra modului în care soluțiile antivirus sunt concepute și implementate în practică.</p><p dir="ltr"><b>References</b></p><p dir="ltr">Paul A. Gagniuc.…”
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  17. 1257
    Image 5_Single-cell and machine learning-based pyroptosis-related gene signature predicts prognosis and immunotherapy response in glioblastoma.tif

    Image 5_Single-cell and machine learning-based pyroptosis-related gene signature predicts prognosis and immunotherapy response in glioblastoma.tif by Liren Fang (22489516)

    Published 2025
    “…Drug sensitivity analyses revealed distinct therapeutic vulnerabilities between subgroups. Functional assays confirmed that MAP1B promotes proliferation, migration, and invasion in GBM cells, reinforcing its oncogenic role.…”
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  18. 1258
    Image 3_Single-cell and machine learning-based pyroptosis-related gene signature predicts prognosis and immunotherapy response in glioblastoma.tif

    Image 3_Single-cell and machine learning-based pyroptosis-related gene signature predicts prognosis and immunotherapy response in glioblastoma.tif by Liren Fang (22489516)

    Published 2025
    “…Drug sensitivity analyses revealed distinct therapeutic vulnerabilities between subgroups. Functional assays confirmed that MAP1B promotes proliferation, migration, and invasion in GBM cells, reinforcing its oncogenic role.…”
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  19. 1259
    Table 2_Single-cell and machine learning-based pyroptosis-related gene signature predicts prognosis and immunotherapy response in glioblastoma.xlsx

    Table 2_Single-cell and machine learning-based pyroptosis-related gene signature predicts prognosis and immunotherapy response in glioblastoma.xlsx by Liren Fang (22489516)

    Published 2025
    “…Drug sensitivity analyses revealed distinct therapeutic vulnerabilities between subgroups. Functional assays confirmed that MAP1B promotes proliferation, migration, and invasion in GBM cells, reinforcing its oncogenic role.…”
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  20. 1260
    Image 1_Single-cell and machine learning-based pyroptosis-related gene signature predicts prognosis and immunotherapy response in glioblastoma.tif

    Image 1_Single-cell and machine learning-based pyroptosis-related gene signature predicts prognosis and immunotherapy response in glioblastoma.tif by Liren Fang (22489516)

    Published 2025
    “…Drug sensitivity analyses revealed distinct therapeutic vulnerabilities between subgroups. Functional assays confirmed that MAP1B promotes proliferation, migration, and invasion in GBM cells, reinforcing its oncogenic role.…”
    Save to List
    Saved in:

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