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Showing 1 - 20 results of 126 for search '(( binary based whole optimization algorithm ) OR ( lines based sample optimization algorithm ))', query time: 0.58s Refine Results
  1. 1
    Spearman correlation coefficient results for classification algorithms (predicted probabilities) and regression algorithms (predicted IC50 values).

    Spearman correlation coefficient results for classification algorithms (predicted probabilities) and regression algorithms (predicted IC50 values). by Sofia P. Miranda (11418930)

    Published 2021
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  2. 2
    Classification results for all subsampling scenarios and algorithms for Gefitinib.

    Classification results for all subsampling scenarios and algorithms for Gefitinib. by Sofia P. Miranda (11418930)

    Published 2021
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  3. 3
    Sensitivity analysis of the TV-BayesOpt algorithm with a forgetting (orange line) or a forgetting-periodic (blue line) covariance function for a range of ε values.

    Sensitivity analysis of the TV-BayesOpt algorithm with a forgetting (orange line) or a forgetting-periodic (blue line) covariance function for a range of ε values. by John E. Fleming (8533956)

    Published 2023
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  4. 4
    Regression results for all combinations of subsampling scenarios and algorithms for Gefitinib.

    Regression results for all combinations of subsampling scenarios and algorithms for Gefitinib. by Sofia P. Miranda (11418930)

    Published 2021
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  5. 5
    Average RMSE rank for all combinations of subsampling scenarios and algorithms.

    Average RMSE rank for all combinations of subsampling scenarios and algorithms. by Sofia P. Miranda (11418930)

    Published 2021
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  6. 6
    Summary of AUC values across all combinations of subsampling scenario and algorithm.

    Summary of AUC values across all combinations of subsampling scenario and algorithm. by Sofia P. Miranda (11418930)

    Published 2021
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  7. 7
    Most informative genes for predicting cell-line responses for Gefitinib.

    Most informative genes for predicting cell-line responses for Gefitinib. by Sofia P. Miranda (11418930)

    Published 2021
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  8. 8
    Descriptions of the algorithms we tested and hyperparameters that we evaluated via nested cross validation.

    Descriptions of the algorithms we tested and hyperparameters that we evaluated via nested cross validation. by Sofia P. Miranda (11418930)

    Published 2021
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  9. 9
    Minimum, mean and maximum RMSE value for each drug and algorithm combination, averaged across all subsampling scenarios.

    Minimum, mean and maximum RMSE value for each drug and algorithm combination, averaged across all subsampling scenarios. by Sofia P. Miranda (11418930)

    Published 2021
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  10. 10
    Minimum, mean and maximum AUC value for each combination of drug and algorithm, averaged across all subsampling scenarios.

    Minimum, mean and maximum AUC value for each combination of drug and algorithm, averaged across all subsampling scenarios. by Sofia P. Miranda (11418930)

    Published 2021
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  11. 11
    Predicting patient drug response from cell-line methylation profiles for Temozolomide (n = 85).

    Predicting patient drug response from cell-line methylation profiles for Temozolomide (n = 85). by Sofia P. Miranda (11418930)

    Published 2021
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  12. 12
    Image_1_UWB indoor positioning optimization algorithm based on genetic annealing and clustering analysis.jpg

    Image_1_UWB indoor positioning optimization algorithm based on genetic annealing and clustering analysis.jpg by Hua Guo (305546)

    Published 2022
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  13. 13
    Image_5_UWB indoor positioning optimization algorithm based on genetic annealing and clustering analysis.jpg

    Image_5_UWB indoor positioning optimization algorithm based on genetic annealing and clustering analysis.jpg by Hua Guo (305546)

    Published 2022
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  14. 14
    Image_2_UWB indoor positioning optimization algorithm based on genetic annealing and clustering analysis.jpg

    Image_2_UWB indoor positioning optimization algorithm based on genetic annealing and clustering analysis.jpg by Hua Guo (305546)

    Published 2022
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  15. 15
    Image_3_UWB indoor positioning optimization algorithm based on genetic annealing and clustering analysis.jpg

    Image_3_UWB indoor positioning optimization algorithm based on genetic annealing and clustering analysis.jpg by Hua Guo (305546)

    Published 2022
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  16. 16
    Image_4_UWB indoor positioning optimization algorithm based on genetic annealing and clustering analysis.jpg

    Image_4_UWB indoor positioning optimization algorithm based on genetic annealing and clustering analysis.jpg by Hua Guo (305546)

    Published 2022
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  17. 17
    Histograms for each drug based on drug response (IC<sub>50</sub> values) for the GDSC dataset.

    Histograms for each drug based on drug response (IC<sub>50</sub> values) for the GDSC dataset. by Sofia P. Miranda (11418930)

    Published 2021
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  18. 18
    Correlation between predicted drug responses based on GDSC cell lines and recorded clinical responses in TCGA patients for selected combinations of subsampling scenarios and algorithms across all drugs.

    Correlation between predicted drug responses based on GDSC cell lines and recorded clinical responses in TCGA patients for selected combinations of subsampling scenarios and algori... by Sofia P. Miranda (11418930)

    Published 2021
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  19. 19
    Results of simulating the ability of pBO, BO and random search algorithms on identifying the optima in the Hartmann 3-dimensional surface.

    Results of simulating the ability of pBO, BO and random search algorithms on identifying the optima in the Hartmann 3-dimensional surface. by Nienke E. R. van Bueren (11414330)

    Published 2021
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  20. 20
    Linear-regression-based algorithms succeed at identifying the correct functional groups in synthetic data, and multi-group algorithms recover more information.

    Linear-regression-based algorithms succeed at identifying the correct functional groups in synthetic data, and multi-group algorithms recover more information. by Yuanchen Zhao (12905580)

    Published 2024
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