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Showing 1 - 20 results of 9,623 for search '(((( algorithm from function ) OR ( algorithm l function ))) OR ( algorithm etc function ))', query time: 0.63s Refine Results
  1. 1
    Comparison of details of different algorithms.

    Comparison of details of different algorithms. by Hao Wu (65943)

    Published 2024
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  2. 2
    Structure of the LMPEC algorithm model.

    Structure of the LMPEC algorithm model. by Hao Wu (65943)

    Published 2024
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  3. 3
    Results from different ML algorithms.

    Results from different ML algorithms. by Caroline L. Alves (14271413)

    Published 2024
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  4. 4
    Flexible CDOCKER: Hybrid Searching Algorithm and Scoring Function with Side Chain Conformational Entropy

    Flexible CDOCKER: Hybrid Searching Algorithm and Scoring Function with Side Chain Conformational Entropy by Yujin Wu (2901128)

    Published 2021
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  5. 5
    Flexible CDOCKER: Hybrid Searching Algorithm and Scoring Function with Side Chain Conformational Entropy

    Flexible CDOCKER: Hybrid Searching Algorithm and Scoring Function with Side Chain Conformational Entropy by Yujin Wu (2901128)

    Published 2021
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  6. 6
    Absolute error of blur length <i>L</i> from our proposed method.

    Absolute error of blur length <i>L</i> from our proposed method. by Wei Zhou (24328)

    Published 2020
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  7. 7
    Algorithm detection steps.

    Algorithm detection steps. by José M. Rivera-Arbeláez (12418512)

    Published 2025
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  8. 8
    Algorithmic assessment reveals functional implications of GABRD gene variants linked to idiopathic generalized epilepsy

    Algorithmic assessment reveals functional implications of GABRD gene variants linked to idiopathic generalized epilepsy by Ayla Arslan (17943365)

    Published 2024
    “…</p> <p>The study identifies specific variants (L111R, R114C, D123N, G150S, and L243P) in the coding region of the GABRD gene, which are predicted as deleterious by multiple algorithms. …”
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  9. 9
    S1 Dataset -

    S1 Dataset - by Hao Wu (65943)

    Published 2024
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  10. 10
    Test results of different training methods.

    Test results of different training methods. by Hao Wu (65943)

    Published 2024
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  11. 11
    Schematic diagram of subpixel convolution.

    Schematic diagram of subpixel convolution. by Hao Wu (65943)

    Published 2024
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  12. 12
    PS-UNet++ model structure.

    PS-UNet++ model structure. by Hao Wu (65943)

    Published 2024
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  13. 13
    Network structure diagram of UNet++.

    Network structure diagram of UNet++. by Hao Wu (65943)

    Published 2024
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  14. 14
    Sub-pixel convolution upsampling module.

    Sub-pixel convolution upsampling module. by Hao Wu (65943)

    Published 2024
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  15. 15
    Proportion of simulated data with improved likelihood from using multiple restarts (Algorithm 1).

    Proportion of simulated data with improved likelihood from using multiple restarts (Algorithm 1). by Jesse Wheeler (18462457)

    Published 2025
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  16. 16
    Functional validation of the metastasis-predictive genes inferred by PLUS from TCGA data.

    Functional validation of the metastasis-predictive genes inferred by PLUS from TCGA data. by Junyi Zhou (495644)

    Published 2022
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  17. 17
    Comparison of the top five upregulated and top five downregulated molecular function GO terms of Unipept with the molecular function GO terms from the other tools from the oral dysbiosis dataset.

    Comparison of the top five upregulated and top five downregulated molecular function GO terms of Unipept with the molecular function GO terms from the other tools from the oral dys... by Ray Sajulga (5721518)

    Published 2020
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  18. 18
    Multimodal reference functions.

    Multimodal reference functions. by Ruiyu Zhan (21602031)

    Published 2025
    “…Utilizing the diabetes dataset from 130 U.S. hospitals, the LGWO-BP algorithm achieved a precision rate of 0.97, a sensitivity of 1.00, a correct classification rate of 0.99, a harmonic mean of precision and recall (F1-score) of 0.98, and an area under the ROC curve (AUC) of 1.00. …”
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  19. 19
    Sub-model-specific gene sets with functional annotations and weights.

    Sub-model-specific gene sets with functional annotations and weights. by Josh L. Espinoza (10492448)

    Published 2021
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  20. 20
    Spectrograms extracted from the generalized S-transform corresponding to the adjustment parameters λ<sub><i>G</i></sub> = 0.05, λ<sub><i>L</i></sub> = 0.01.

    Spectrograms extracted from the generalized S-transform corresponding to the adjustment parameters λ<sub><i>G</i></sub> = 0.05, λ<sub><i>L</i></sub> = 0.01. by Li Jiashen (20552690)

    Published 2025
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