Showing 61 - 73 results of 73 for search '(( primary role model optimization algorithm ) OR ( binary basic process optimization algorithm ))*', query time: 0.33s Refine Results
  1. 61

    Image 4_Integrative single-cell and exosomal multi-omics uncovers SCNN1A and EFNA1 as non-invasive biomarkers and drivers of ovarian cancer metastasis.pdf by Liping Tang (77094)

    Published 2025
    “…We then applied ten machine learning algorithm to exosomal transcriptomic data to evaluate diagnostic performance and identify the optimal classifier. …”
  2. 62

    Image 1_Integrative single-cell and exosomal multi-omics uncovers SCNN1A and EFNA1 as non-invasive biomarkers and drivers of ovarian cancer metastasis.tif by Liping Tang (77094)

    Published 2025
    “…We then applied ten machine learning algorithm to exosomal transcriptomic data to evaluate diagnostic performance and identify the optimal classifier. …”
  3. 63

    Image 7_Integrative single-cell and exosomal multi-omics uncovers SCNN1A and EFNA1 as non-invasive biomarkers and drivers of ovarian cancer metastasis.tif by Liping Tang (77094)

    Published 2025
    “…We then applied ten machine learning algorithm to exosomal transcriptomic data to evaluate diagnostic performance and identify the optimal classifier. …”
  4. 64

    Image 2_Integrative single-cell and exosomal multi-omics uncovers SCNN1A and EFNA1 as non-invasive biomarkers and drivers of ovarian cancer metastasis.pdf by Liping Tang (77094)

    Published 2025
    “…We then applied ten machine learning algorithm to exosomal transcriptomic data to evaluate diagnostic performance and identify the optimal classifier. …”
  5. 65

    Image 3_Integrative single-cell and exosomal multi-omics uncovers SCNN1A and EFNA1 as non-invasive biomarkers and drivers of ovarian cancer metastasis.pdf by Liping Tang (77094)

    Published 2025
    “…We then applied ten machine learning algorithm to exosomal transcriptomic data to evaluate diagnostic performance and identify the optimal classifier. …”
  6. 66

    Data Sheet 1_Integrative single-cell and exosomal multi-omics uncovers SCNN1A and EFNA1 as non-invasive biomarkers and drivers of ovarian cancer metastasis.zip by Liping Tang (77094)

    Published 2025
    “…We then applied ten machine learning algorithm to exosomal transcriptomic data to evaluate diagnostic performance and identify the optimal classifier. …”
  7. 67

    Image 5_Integrative single-cell and exosomal multi-omics uncovers SCNN1A and EFNA1 as non-invasive biomarkers and drivers of ovarian cancer metastasis.pdf by Liping Tang (77094)

    Published 2025
    “…We then applied ten machine learning algorithm to exosomal transcriptomic data to evaluate diagnostic performance and identify the optimal classifier. …”
  8. 68

    Image 6_Integrative single-cell and exosomal multi-omics uncovers SCNN1A and EFNA1 as non-invasive biomarkers and drivers of ovarian cancer metastasis.pdf by Liping Tang (77094)

    Published 2025
    “…We then applied ten machine learning algorithm to exosomal transcriptomic data to evaluate diagnostic performance and identify the optimal classifier. …”
  9. 69

    Table 1_Association between pro-inflammatory diet and fecal incontinence: a large population-based study.pdf by Haiyang Wang (22389)

    Published 2025
    “…Background<p>It is widely acknowledged that dietary habits play a pivotal role in maintaining optimal intestinal health. Fecal incontinence (FI) is a distressing and often stigmatized inflammatory ailment with a strong correlation to a multitude of intestinal disorders. …”
  10. 70

    Image 1_Association between pro-inflammatory diet and fecal incontinence: a large population-based study.pdf by Haiyang Wang (22389)

    Published 2025
    “…Background<p>It is widely acknowledged that dietary habits play a pivotal role in maintaining optimal intestinal health. Fecal incontinence (FI) is a distressing and often stigmatized inflammatory ailment with a strong correlation to a multitude of intestinal disorders. …”
  11. 71

    Machine Learning-Ready Dataset for Cytotoxicity Prediction of Metal Oxide Nanoparticles by Soham Savarkar (21811825)

    Published 2025
    “…</p><p dir="ltr">Encoding: Categorical variables such as surface coating and cell type were grouped into logical classes and label-encoded to enable model compatibility.</p><p dir="ltr"><b>Applications and Model Compatibility:</b></p><p dir="ltr">The dataset is optimized for use in supervised learning workflows and has been tested with algorithms such as:</p><p dir="ltr">Gradient Boosting Machines (GBM),</p><p dir="ltr">Support Vector Machines (SVM-RBF),</p><p dir="ltr">Random Forests, and</p><p dir="ltr">Principal Component Analysis (PCA) for feature reduction.…”
  12. 72

    Image 1_Correlation between metformin use and mortality in acute respiratory failure: a retrospective ICU cohort study.tif by Yunlin Yang (10277429)

    Published 2025
    “…We applied Kaplan–Meier survival curves, Cox proportional hazards models, and logistic regression to assess associations. …”
  13. 73

    Image 2_Correlation between metformin use and mortality in acute respiratory failure: a retrospective ICU cohort study.tif by Yunlin Yang (10277429)

    Published 2025
    “…We applied Kaplan–Meier survival curves, Cox proportional hazards models, and logistic regression to assess associations. …”