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Showing 1 - 20 results of 57 for search '(( binary data process segmentation algorithm ) OR ( binary arm design optimization algorithm ))*', query time: 0.70s Refine Results
  1. 1
    The overall accuracy of the binary RF classifications of the RSK region, Morocco from 1985 to 2020.

    The overall accuracy of the binary RF classifications of the RSK region, Morocco from 1985 to 2020. by Safia Loulad (16913691)

    Published 2023
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  2. 2
    Random forest binary classification of the colorized Corona image for the RSK region, Morocco (1972).

    Random forest binary classification of the colorized Corona image for the RSK region, Morocco (1972). by Safia Loulad (16913691)

    Published 2023
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  3. 3
    Joint Detection of Change Points in Multichannel Single-Molecule Measurements

    Joint Detection of Change Points in Multichannel Single-Molecule Measurements by Hugh Wilson (11797444)

    Published 2021
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  4. 4
    tUbenet Foundation Model Weights

    tUbenet Foundation Model Weights by Natalie Holroyd (7099391)

    Published 2025
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  5. 5
    Triplet Matching for Estimating Causal Effects With Three Treatment Arms: A Comparative Study of Mortality by Trauma Center Level

    Triplet Matching for Estimating Causal Effects With Three Treatment Arms: A Comparative Study of Mortality by Trauma Center Level by Giovanni Nattino (561797)

    Published 2021
    “…Propensity score matching is a popular method to infer causal relationships in observational studies with two treatment arms. Few studies, however, have used matching designs with more than two groups, due to the complexity of matching algorithms. …”
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  6. 6
    The performance of our proposed method in four scenarios (I to IV) uses simulated data that mimics real-world data distribution with the complete version.

    The performance of our proposed method in four scenarios (I to IV) uses simulated data that mimics real-world data distribution with the complete version. by Yi-Wen Hsiao (13767916)

    Published 2022
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  7. 7
    Schematic representation of semi-automated algorithm for organoids analyzing.

    Schematic representation of semi-automated algorithm for organoids analyzing. by Anna Demchenko (18449439)

    Published 2025
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  8. 8
    Performance of our proposed method in four scenarios (I to IV) using naïve simulated data with the complete version.

    Performance of our proposed method in four scenarios (I to IV) using naïve simulated data with the complete version. by Yi-Wen Hsiao (13767916)

    Published 2022
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  9. 9
    Performance of the ceRNAR package using synthetic data with randomly selected correlation values from 0.8 to 0.9.

    Performance of the ceRNAR package using synthetic data with randomly selected correlation values from 0.8 to 0.9. by Yi-Wen Hsiao (13767916)

    Published 2022
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  10. 10
    Performance comparisons between ceRNIA, JAMI, SPONGE, GDCRNATools and ceRNAR packages using synthetic data that mimic the real-world case.

    Performance comparisons between ceRNIA, JAMI, SPONGE, GDCRNATools and ceRNAR packages using synthetic data that mimic the real-world case. by Yi-Wen Hsiao (13767916)

    Published 2022
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  11. 11
    Performance of the ceRNAR package in five scenarios under the complete version using synthetic data with a fixed correlation value (0.9).

    Performance of the ceRNAR package in five scenarios under the complete version using synthetic data with a fixed correlation value (0.9). by Yi-Wen Hsiao (13767916)

    Published 2022
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  12. 12
    Performance of the ceRNAR package in five scenarios under the fast version using synthetic data with a fixed correlation value (0.9).

    Performance of the ceRNAR package in five scenarios under the fast version using synthetic data with a fixed correlation value (0.9). by Yi-Wen Hsiao (13767916)

    Published 2022
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  13. 13
    Performance of the ceRNAR package in five scenarios under the complete version using synthetic data with a fixed correlation value (0.6).

    Performance of the ceRNAR package in five scenarios under the complete version using synthetic data with a fixed correlation value (0.6). by Yi-Wen Hsiao (13767916)

    Published 2022
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  14. 14
    Performance of the ceRNAR package in five scenarios under the complete version using synthetic data with a fixed correlation value (0.3).

    Performance of the ceRNAR package in five scenarios under the complete version using synthetic data with a fixed correlation value (0.3). by Yi-Wen Hsiao (13767916)

    Published 2022
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  15. 15
    A graphic overview illustrates the random walk and permutation test in the <i>ceRNApairFiltering</i> method.

    A graphic overview illustrates the random walk and permutation test in the <i>ceRNApairFiltering</i> method. by Yi-Wen Hsiao (13767916)

    Published 2022
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  16. 16
    A graphic overview illustrates sorting and correlation calculation in the <i>ceRNApairFiltering</i> method.

    A graphic overview illustrates sorting and correlation calculation in the <i>ceRNApairFiltering</i> method. by Yi-Wen Hsiao (13767916)

    Published 2022
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  17. 17
    Run time comparisons between published tools and our package.

    Run time comparisons between published tools and our package. by Yi-Wen Hsiao (13767916)

    Published 2022
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  18. 18
    Performance of the ceRNAR package in five scenarios under the complete version using synthetic data with randomly selected correlation values from 0.3 to 0.9.

    Performance of the ceRNAR package in five scenarios under the complete version using synthetic data with randomly selected correlation values from 0.3 to 0.9. by Yi-Wen Hsiao (13767916)

    Published 2022
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  19. 19
    Performance of the ceRNAR package with different proportions of correlated samples in scenario III under the fast version using synthetic data with a fixed correlation value (0.9).

    Performance of the ceRNAR package with different proportions of correlated samples in scenario III under the fast version using synthetic data with a fixed correlation value (0.9). by Yi-Wen Hsiao (13767916)

    Published 2022
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  20. 20
    The meanings of score S are based on the values of P<sub>obey</sub> and P<sub>violate</sub>.

    The meanings of score S are based on the values of P<sub>obey</sub> and P<sub>violate</sub>. by Yi-Wen Hsiao (13767916)

    Published 2022
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