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Showing 1 - 20 results of 55 for search '(( lines based cohort optimization algorithm ) OR ( binary _ codon optimization algorithm ))', query time: 0.41s Refine Results
  1. 1
    Calibration plots for all models on the Derivation cohort.

    Calibration plots for all models on the Derivation cohort. by Agni Orfanoudaki (8878097)

    Published 2020
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  2. 2
    Selecting an optimal machine learning classifier.

    Selecting an optimal machine learning classifier. by Ramu Anandakrishnan (294535)

    Published 2024
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  3. 3
    Manhattan plot of the univariate <i>p</i>-values for body mass index.

    Manhattan plot of the univariate <i>p</i>-values for body mass index. by Junyang Qian (9558026)

    Published 2020
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  4. 4
    Manhattan plot of the univariate <i>p</i>-values for asthma.

    Manhattan plot of the univariate <i>p</i>-values for asthma. by Junyang Qian (9558026)

    Published 2020
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  5. 5
    Manhattan plot of the univariate <i>p</i>-values for height.

    Manhattan plot of the univariate <i>p</i>-values for height. by Junyang Qian (9558026)

    Published 2020
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  6. 6
    Manhattan plot of the univariate <i>p</i>-values for high cholesterol.

    Manhattan plot of the univariate <i>p</i>-values for high cholesterol. by Junyang Qian (9558026)

    Published 2020
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  7. 7
    Dual UHPLC-HRMS Metabolomics and Lipidomics and Automated Data Processing Workflow for Comprehensive High-Throughput Gut Phenotyping

    Dual UHPLC-HRMS Metabolomics and Lipidomics and Automated Data Processing Workflow for Comprehensive High-Throughput Gut Phenotyping by P. Vangeenderhuysen (15854812)

    Published 2023
    “…The latter was benchmarked toward vendor-specific targeted and untargeted software and our isotopologue parameter optimization/XCMS-based untargeted pipeline in LifeLines Deep cohort samples (<i>n</i> = 97). …”
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  8. 8
    A. Top – 14 days of continuous glucose monitor (CGM) data for a patient that spends all of their time within an optimal glucose range.

    A. Top – 14 days of continuous glucose monitor (CGM) data for a patient that spends all of their time within an optimal glucose range. by Jamison H. Burks (21029607)

    Published 2025
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  9. 9
    Establishment of MPM.

    Establishment of MPM. by Amir Abbas Navidinia (21656136)

    Published 2025
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  10. 10
    DataSheet2_Concordance of the treatment patterns for major depressive disorders between the Canadian Network for Mood and Anxiety Treatments (CANMAT) algorithm and real-world practice in China.pdf

    DataSheet2_Concordance of the treatment patterns for major depressive disorders between the Canadian Network for Mood and Anxiety Treatments (CANMAT) algorithm and real-world pract... by Lu Yang (30847)

    Published 2022
    “…</p><p>Conclusion: Gap existed between clinical practice and AD algorithm. Improved access to evidence-based treatment is required, especially for optimized strategies during outpatient follow-up.…”
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  11. 11
    DataSheet1_Concordance of the treatment patterns for major depressive disorders between the Canadian Network for Mood and Anxiety Treatments (CANMAT) algorithm and real-world practice in China.docx

    DataSheet1_Concordance of the treatment patterns for major depressive disorders between the Canadian Network for Mood and Anxiety Treatments (CANMAT) algorithm and real-world pract... by Lu Yang (30847)

    Published 2022
    “…</p><p>Conclusion: Gap existed between clinical practice and AD algorithm. Improved access to evidence-based treatment is required, especially for optimized strategies during outpatient follow-up.…”
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  12. 12
    Methodology block diagram.

    Methodology block diagram. by Gahao Chen (21688843)

    Published 2025
    “…Six machine learning algorithms - Random Forest (RF), AdaBoost, Light Gradient Boosting Machine (LightGBM), eXtreme Gradient Boosting (XGBoost), Categorical Boosting (CatBoost), and Tabular Prior-data Fitted Network version 2.0 (TabPFN-V2) - were implemented with five-fold cross-validation to optimize model hyperparameters. …”
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  13. 13
    Table_4_Computed Tomography Imaging-Based Radiogenomics Analysis Reveals Hypoxia Patterns and Immunological Characteristics in Ovarian Cancer.xlsx

    Table_4_Computed Tomography Imaging-Based Radiogenomics Analysis Reveals Hypoxia Patterns and Immunological Characteristics in Ovarian Cancer.xlsx by Songwei Feng (11200268)

    Published 2022
    “…Finally, optimal radiogenomics biomarkers for predicting the risk status of patients were developed by machine learning algorithms.…”
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  14. 14
    Image_2_Computed Tomography Imaging-Based Radiogenomics Analysis Reveals Hypoxia Patterns and Immunological Characteristics in Ovarian Cancer.tiff

    Image_2_Computed Tomography Imaging-Based Radiogenomics Analysis Reveals Hypoxia Patterns and Immunological Characteristics in Ovarian Cancer.tiff by Songwei Feng (11200268)

    Published 2022
    “…Finally, optimal radiogenomics biomarkers for predicting the risk status of patients were developed by machine learning algorithms.…”
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  15. 15
    Table_2_Computed Tomography Imaging-Based Radiogenomics Analysis Reveals Hypoxia Patterns and Immunological Characteristics in Ovarian Cancer.xlsx

    Table_2_Computed Tomography Imaging-Based Radiogenomics Analysis Reveals Hypoxia Patterns and Immunological Characteristics in Ovarian Cancer.xlsx by Songwei Feng (11200268)

    Published 2022
    “…Finally, optimal radiogenomics biomarkers for predicting the risk status of patients were developed by machine learning algorithms.…”
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  16. 16
    Image_1_Computed Tomography Imaging-Based Radiogenomics Analysis Reveals Hypoxia Patterns and Immunological Characteristics in Ovarian Cancer.tiff

    Image_1_Computed Tomography Imaging-Based Radiogenomics Analysis Reveals Hypoxia Patterns and Immunological Characteristics in Ovarian Cancer.tiff by Songwei Feng (11200268)

    Published 2022
    “…Finally, optimal radiogenomics biomarkers for predicting the risk status of patients were developed by machine learning algorithms.…”
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  17. 17
    Image_4_Computed Tomography Imaging-Based Radiogenomics Analysis Reveals Hypoxia Patterns and Immunological Characteristics in Ovarian Cancer.tiff

    Image_4_Computed Tomography Imaging-Based Radiogenomics Analysis Reveals Hypoxia Patterns and Immunological Characteristics in Ovarian Cancer.tiff by Songwei Feng (11200268)

    Published 2022
    “…Finally, optimal radiogenomics biomarkers for predicting the risk status of patients were developed by machine learning algorithms.…”
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  18. 18
    Table_1_Computed Tomography Imaging-Based Radiogenomics Analysis Reveals Hypoxia Patterns and Immunological Characteristics in Ovarian Cancer.xlsx

    Table_1_Computed Tomography Imaging-Based Radiogenomics Analysis Reveals Hypoxia Patterns and Immunological Characteristics in Ovarian Cancer.xlsx by Songwei Feng (11200268)

    Published 2022
    “…Finally, optimal radiogenomics biomarkers for predicting the risk status of patients were developed by machine learning algorithms.…”
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  19. 19
    Image_3_Computed Tomography Imaging-Based Radiogenomics Analysis Reveals Hypoxia Patterns and Immunological Characteristics in Ovarian Cancer.tiff

    Image_3_Computed Tomography Imaging-Based Radiogenomics Analysis Reveals Hypoxia Patterns and Immunological Characteristics in Ovarian Cancer.tiff by Songwei Feng (11200268)

    Published 2022
    “…Finally, optimal radiogenomics biomarkers for predicting the risk status of patients were developed by machine learning algorithms.…”
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    Saved in:
  20. 20
    Table_3_Computed Tomography Imaging-Based Radiogenomics Analysis Reveals Hypoxia Patterns and Immunological Characteristics in Ovarian Cancer.xlsx

    Table_3_Computed Tomography Imaging-Based Radiogenomics Analysis Reveals Hypoxia Patterns and Immunological Characteristics in Ovarian Cancer.xlsx by Songwei Feng (11200268)

    Published 2022
    “…Finally, optimal radiogenomics biomarkers for predicting the risk status of patients were developed by machine learning algorithms.…”
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    Saved in:

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