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Showing 1 - 20 results of 564 for search '(( python effective implementation ) OR ( python model represented ))', query time: 0.36s Refine Results
  1. 1
    Effect of <i>|Δd</i><sub><i>n</i></sub><i>|</i> perturbation on 3D modeling.

    Effect of <i>|Δd</i><sub><i>n</i></sub><i>|</i> perturbation on 3D modeling. by Giacomo Janson (8138517)

    Published 2019
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  2. 2
    Total epithelial chloride fluxes for the W-PCT-E model.

    Total epithelial chloride fluxes for the W-PCT-E model. by Leyla Noroozbabaee (14100772)

    Published 2022
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  3. 3
    Python code.

    Python code. by Kapil Newar (6533048)

    Published 2022
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  4. 4
    Accuracy of the segmentation model.

    Accuracy of the segmentation model. by Owen M. O’Connor (11965460)

    Published 2022
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  5. 5
    Listed python codes of our algorithm with explanations (Python 3.9 recommended).

    Listed python codes of our algorithm with explanations (Python 3.9 recommended). by Takayuki Niizato (162226)

    Published 2023
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  6. 6
    Table_1_An application programming interface implementing Bayesian approaches for evaluating effect of time-varying treatment with R and Python.pdf

    Table_1_An application programming interface implementing Bayesian approaches for evaluating effect of time-varying treatment with R and Python.pdf by Chen Chen (6544)

    Published 2023
    “…Here, we introduce an application programming interface (API) that implements both BART and GP for estimating averaged treatment effect (ATE) and conditional averaged treatment (CATE) for the two-stage time-varying treatment strategies.…”
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  7. 7
    Reducing the size of the model to increase speed.

    Reducing the size of the model to increase speed. by Owen M. O’Connor (11965460)

    Published 2022
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  8. 8
    Training the model on segmentation with custom weight maps.

    Training the model on segmentation with custom weight maps. by Owen M. O’Connor (11965460)

    Published 2022
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  9. 9
    Impact of frame shifts on tracking model performance.

    Impact of frame shifts on tracking model performance. by Owen M. O’Connor (11965460)

    Published 2022
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  10. 10
    Python sript.

    Python sript. by Kan Jia (6438599)

    Published 2025
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  11. 11
    SCADET algorithm parameter configuration (Python implementation).

    SCADET algorithm parameter configuration (Python implementation). by Xiaolong Zhang (470454)

    Published 2025
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  12. 12
    Utilizing variable background weight maps for training the model on debris.

    Utilizing variable background weight maps for training the model on debris. by Owen M. O’Connor (11965460)

    Published 2022
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  13. 13
    Loss history of segmentation model trained over 600 epochs.

    Loss history of segmentation model trained over 600 epochs. by Owen M. O’Connor (11965460)

    Published 2022
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  14. 14
    Schematic showing how the tracking model uses the inputs to make predictions.

    Schematic showing how the tracking model uses the inputs to make predictions. by Owen M. O’Connor (11965460)

    Published 2022
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  15. 15
    Schematic of the pixelwise binary cross-entropy loss function used to train the segmentation and tracking models.

    Schematic of the pixelwise binary cross-entropy loss function used to train the segmentation and tracking models. by Owen M. O’Connor (11965460)

    Published 2022
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  16. 16
    Modeling lung cancer AST network and its perturbation with a SOX2 degrader. (A) Lung Cancer AST Network and the corresponding stochastic equations. Parameters are , , , , , , , , with different . The control parameter is . (B) The potential landscape is approximated by simulating a large number of molecules corresponding to concentrations ( samples) with parameter values , , and , respectively representing the adenocarcinoma state, the intermediate state, and the squamous carcinoma state. FOXA2 and SOX2 amounts are chosen to visualize the landscape. (C) Principle of SOX2-targeted degradation by the PROTAC technique. The PROTAC molecule here is designed to recognize SOX2 proteins and to recruit E3 ligases to induce an addition of ubiquitin (Ub) proteins onto SOX2, which causes degradation of SOX2 subsequently. (D) Application of a SOX2 degrader to the squamous carcinoma state () reprogram the AST network back to the adenocarcinoma state. Here the treatment effect of the SOX2 degrad

    Modeling lung cancer AST network and its perturbation with a SOX2 degrader. (A) Lung Cancer AST Network and the corresponding stochastic equations. Parameters are , , , , , , , , w... by Ziyan Jin (21021976)

    Published 2025
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  17. 17
    Implementations benchmark.

    Implementations benchmark. by Serena Aneli (17002149)

    Published 2023
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  18. 18
    Tools used in implementing the RNN models.

    Tools used in implementing the RNN models. by Fahmi H. Quradaa (17896527)

    Published 2024
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  19. 19
    Conceptual of integrating FEM simulation in a Python scripting interface.

    Conceptual of integrating FEM simulation in a Python scripting interface. by Chang Soon Kim (17480874)

    Published 2023
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  20. 20
    Model comparison using the Widely Applicable Information Criterion (WAIC).

    Model comparison using the Widely Applicable Information Criterion (WAIC). by Sophia C. M. Orzechowski (6577349)

    Published 2019
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