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Showing 101 - 120 results of 12,967 for search '(((( algorithm i function ) OR ( algorithm module function ))) OR ( algorithm python function ))*', query time: 0.66s Refine Results
  1. 101
    Brain activity modulations during online application in task → happy averaged across participants P01, P04, and P05.

    Brain activity modulations during online application in task → happy averaged across participants P01, P04, and P05. by Stefan K. Ehrlich (6475490)

    Published 2019
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  2. 102
    Optimization results for parameter [mini-batch, epochs, and standard deviation of domain filter <i>σ</i><sub><i>d</i></sub>] determination.

    Optimization results for parameter [mini-batch, epochs, and standard deviation of domain filter <i>σ</i><sub><i>d</i></sub>] determination. by Tsutomu Gomi (7382183)

    Published 2020
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  3. 103
    C3 Module.

    C3 Module. by Jiexiang Yang (19980594)

    Published 2025
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  4. 104
    Ghost detect module.

    Ghost detect module. by Jiexiang Yang (19980594)

    Published 2025
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  5. 105
    CageCavityCalc (<i>C</i>3): A Computational Tool for Calculating and Visualizing Cavities in Molecular Cages

    CageCavityCalc (<i>C</i>3): A Computational Tool for Calculating and Visualizing Cavities in Molecular Cages by Vicente Martí-Centelles (1422415)

    Published 2024
    “…Efficiently predicting such properties is critical for accelerating the discovery of novel functional cages. Herein, we introduce <i>CageCavityCalc</i> (<i>C</i>3), a Python-based tool for calculating the cavity size of molecular cages. …”
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  6. 106
    CageCavityCalc (<i>C</i>3): A Computational Tool for Calculating and Visualizing Cavities in Molecular Cages

    CageCavityCalc (<i>C</i>3): A Computational Tool for Calculating and Visualizing Cavities in Molecular Cages by Vicente Martí-Centelles (1422415)

    Published 2024
    “…Efficiently predicting such properties is critical for accelerating the discovery of novel functional cages. Herein, we introduce <i>CageCavityCalc</i> (<i>C</i>3), a Python-based tool for calculating the cavity size of molecular cages. …”
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  7. 107
    CageCavityCalc (<i>C</i>3): A Computational Tool for Calculating and Visualizing Cavities in Molecular Cages

    CageCavityCalc (<i>C</i>3): A Computational Tool for Calculating and Visualizing Cavities in Molecular Cages by Vicente Martí-Centelles (1422415)

    Published 2024
    “…Efficiently predicting such properties is critical for accelerating the discovery of novel functional cages. Herein, we introduce <i>CageCavityCalc</i> (<i>C</i>3), a Python-based tool for calculating the cavity size of molecular cages. …”
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  8. 108
    Effectiveness of SELEX tests <i>in silico</i>.

    Effectiveness of SELEX tests <i>in silico</i>. by Alexander V. Spirov (11302085)

    Published 2022
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  9. 109
    Performance of ENQUIRE’s gene normalization algorithm.

    Performance of ENQUIRE’s gene normalization algorithm. by Luca Musella (19986312)

    Published 2025
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  10. 110
    Functional Connectivity for Experiment and Simulated Anesthesia and Reversal.

    Functional Connectivity for Experiment and Simulated Anesthesia and Reversal. by Bolaji P. Eniwaye (12915339)

    Published 2022
    Subjects: “…differential evolution algorithm…”
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  11. 111
    Effectiveness of evolutionary GA search (mutations only) with (<i>μ</i>, <i>λ</i>) selection, <i>μ</i>/<i>λ</i> = 0.4, 4 domains, 6+4 defined positions out of 26, W = 220 at varying mutation rate and population size as a function of mutation rate and population size.

    Effectiveness of evolutionary GA search (mutations only) with (<i>μ</i>, <i>λ</i>) selection, <i>μ</i>/<i>λ</i> = 0.4, 4 domains, 6+4 defined positions out of 26, W = 220 at varyin... by Alexander V. Spirov (11302085)

    Published 2022
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  12. 112
    General idea of alternative structures of aptamers binding the same ligand and its specific implementation scheme for the BioRS function.

    General idea of alternative structures of aptamers binding the same ligand and its specific implementation scheme for the BioRS function. by Alexander V. Spirov (11302085)

    Published 2022
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  13. 113
    Functional enrichment analysis of DE-FRGs.

    Functional enrichment analysis of DE-FRGs. by Yan Huang (46805)

    Published 2023
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  14. 114
    The input feature map <i>X</i>, the upsampled feature map <i>X</i>′, the generated offsets <i>O</i>, and the original grid <i>G</i> are depicted.

    The input feature map <i>X</i>, the upsampled feature map <i>X</i>′, the generated offsets <i>O</i>, and the original grid <i>G</i> are depicted. by Weiyu Han (3448742)

    Published 2025
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  15. 115
    Test results of different training methods.

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

    Published 2024
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  16. 116
    Schematic diagram of subpixel convolution.

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

    Published 2024
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  17. 117
    PS-UNet++ model structure.

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

    Published 2024
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  18. 118
    Network structure diagram of UNet++.

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

    Published 2024
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  19. 119
    Data_Sheet_1_Adapting Community Detection Algorithms for Disease Module Identification in Heterogeneous Biological Networks.pdf

    Data_Sheet_1_Adapting Community Detection Algorithms for Disease Module Identification in Heterogeneous Biological Networks.pdf by Beethika Tripathi (6451523)

    Published 2019
    “…Lastly, we demonstrate, with justification, that “overlapping” community detection algorithms should be the preferred choice for disease module identification since several genes participate in multiple biological functions.…”
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  20. 120
    C2f Module.

    C2f Module. by Jiexiang Yang (19980594)

    Published 2025
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