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feature optimization » resource optimization (Expand Search), feature elimination (Expand Search), structure optimization (Expand Search)
whale optimization » swarm optimization (Expand Search)
binary based » library based (Expand Search), linac based (Expand Search), binary mask (Expand Search)
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Showing 1 - 20 results of 115 for search '(( binary based whale optimization algorithm ) OR ( lines based feature optimization algorithm ))*', query time: 0.65s Refine Results
  1. 1
    Greedy optimization algorithm.

    Greedy optimization algorithm. by Alexis Bénichou (19832361)

    Published 2024
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  2. 2
    Optimizing Pharmacokinetic Property Prediction Based on Integrated Datasets and a Deep Learning Approach

    Optimizing Pharmacokinetic Property Prediction Based on Integrated Datasets and a Deep Learning Approach by Xiting Wang (9327613)

    Published 2020
    “…In this study, we considered both dataset and algorithm optimization to address the challenge of predicting OBA-related molecular properties. …”
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  3. 3
    Optimizing Pharmacokinetic Property Prediction Based on Integrated Datasets and a Deep Learning Approach

    Optimizing Pharmacokinetic Property Prediction Based on Integrated Datasets and a Deep Learning Approach by Xiting Wang (9327613)

    Published 2020
    “…In this study, we considered both dataset and algorithm optimization to address the challenge of predicting OBA-related molecular properties. …”
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  4. 4
    Optimizing Pharmacokinetic Property Prediction Based on Integrated Datasets and a Deep Learning Approach

    Optimizing Pharmacokinetic Property Prediction Based on Integrated Datasets and a Deep Learning Approach by Xiting Wang (9327613)

    Published 2020
    “…In this study, we considered both dataset and algorithm optimization to address the challenge of predicting OBA-related molecular properties. …”
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  5. 5
    Relative predictive performance when using default algorithm hyperparameters and all features vs. tuning hyperparameters or selecting features.

    Relative predictive performance when using default algorithm hyperparameters and all features vs. tuning hyperparameters or selecting features. by Stephen R. Piccolo (8367780)

    Published 2022
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  6. 6
    The schematic diagram of key geometric parameters for impeller meridian line structure optimization.

    The schematic diagram of key geometric parameters for impeller meridian line structure optimization. by Ning Yu (358970)

    Published 2025
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  7. 7
    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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  8. 8
    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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  9. 9
    Table2_Identification of Pan-Cancer Biomarkers Based on the Gene Expression Profiles of Cancer Cell Lines.XLSX

    Table2_Identification of Pan-Cancer Biomarkers Based on the Gene Expression Profiles of Cancer Cell Lines.XLSX by ShiJian Ding (11764646)

    Published 2021
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  10. 10
    Table1_Identification of Pan-Cancer Biomarkers Based on the Gene Expression Profiles of Cancer Cell Lines.XLSX

    Table1_Identification of Pan-Cancer Biomarkers Based on the Gene Expression Profiles of Cancer Cell Lines.XLSX by ShiJian Ding (11764646)

    Published 2021
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  11. 11
    Table3_Identification of Pan-Cancer Biomarkers Based on the Gene Expression Profiles of Cancer Cell Lines.XLSX

    Table3_Identification of Pan-Cancer Biomarkers Based on the Gene Expression Profiles of Cancer Cell Lines.XLSX by ShiJian Ding (11764646)

    Published 2021
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  12. 12
    Table4_Identification of Pan-Cancer Biomarkers Based on the Gene Expression Profiles of Cancer Cell Lines.XLSX

    Table4_Identification of Pan-Cancer Biomarkers Based on the Gene Expression Profiles of Cancer Cell Lines.XLSX by ShiJian Ding (11764646)

    Published 2021
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  13. 13
    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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  14. 14
    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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  15. 15
    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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  16. 16
    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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  17. 17
    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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  18. 18
    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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  19. 19
    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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  20. 20
    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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