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Showing 81 - 100 results of 297 for search '(( python new implementation ) OR ( ((python model) OR (python code)) implementing ))', query time: 0.40s Refine Results
  1. 81
    Design and implementation of DelaySSA. The input data for DelaySSA are reaction information, initial states, parameters and delays. Combining these into the simulation function, the temporal molecular states <i>n</i> at specific time <i>t</i> will be generated for further visualization.

    Design and implementation of DelaySSA. The input data for DelaySSA are reaction information, initial states, parameters and delays. Combining these into the simulation function, th... by Ziyan Jin (21021976)

    Published 2025
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  2. 82
    Data&Codes.zip

    Data&Codes.zip by Zer0 Star (20545655)

    Published 2025
    “…</p><p dir="ltr">To facilitate the widespread use of the proposed framework, we have implemented it as the <b><i>ESLocalIndi</i></b> open-source package in Python, making it easily accessible to geographers. …”
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  3. 83
    Configurations used in ALAAMs for undirected networks.

    Configurations used in ALAAMs for undirected networks. by Alex Stivala (8356257)

    Published 2024
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  4. 84
    Type II error rate from estimation of simulated outcomes using the EE algorithm.

    Type II error rate from estimation of simulated outcomes using the EE algorithm. by Alex Stivala (8356257)

    Published 2024
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  5. 85
    Comparison of MPNet and ALAAMEE software for ALAAM estimation.

    Comparison of MPNet and ALAAMEE software for ALAAM estimation. by Alex Stivala (8356257)

    Published 2024
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  6. 86
    Configurations used in ALAAMs for directed networks.

    Configurations used in ALAAMs for directed networks. by Alex Stivala (8356257)

    Published 2024
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  7. 87
    Type I error rate from estimation of simulated outcomes using the EE algorithm.

    Type I error rate from estimation of simulated outcomes using the EE algorithm. by Alex Stivala (8356257)

    Published 2024
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  8. 88
    Example ALAAMEE output.

    Example ALAAMEE output. by Alex Stivala (8356257)

    Published 2024
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  9. 89
    Type II error rate as the number of runs used for each sample is varied.

    Type II error rate as the number of runs used for each sample is varied. by Alex Stivala (8356257)

    Published 2024
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  10. 90
    Parameter estimates and estimated standard errors from the EE algorithm.

    Parameter estimates and estimated standard errors from the EE algorithm. by Alex Stivala (8356257)

    Published 2024
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  11. 91
    Network descriptive statistics for the Deezer networks.

    Network descriptive statistics for the Deezer networks. by Alex Stivala (8356257)

    Published 2024
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  12. 92
    Example ALAAMEE goodness-of-fit output.

    Example ALAAMEE goodness-of-fit output. by Alex Stivala (8356257)

    Published 2024
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  13. 93
    ZILLNB_Model

    ZILLNB_Model by Qinhuan Luo (21288548)

    Published 2025
    “…<p dir="ltr">Acquire latent variables using deep-learning based model implemented in python</p>…”
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  14. 94
    Bursty model and Refractory model simulation results. (A) Bursty model simulation results ( samples). The production of nascent mRNA occurs in bursts and degrade after delay time <i>τ</i>. The burst frequency , the mean burst size and . (B) Refractory model simulation results ( samples). The gene has three states, , and , and can switch from one state to another. Moreover, nascent mRNA is produced only when the gene is in state . The parameters are , , , and <i>τ</i> = 1.

    Bursty model and Refractory model simulation results. (A) Bursty model simulation results ( samples). The production of nascent mRNA occurs in bursts and degrade after delay time <... by Ziyan Jin (21021976)

    Published 2025
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  15. 95
    Flowchart representation of lion optimization algorithm for hyperparameter tuning in the HCAP model.

    Flowchart representation of lion optimization algorithm for hyperparameter tuning in the HCAP model. by Mohanad Faeq Ali (21354273)

    Published 2025
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  16. 96
    DeepB3Pred - code

    DeepB3Pred - code by Saleh Musleh (22263718)

    Published 2025
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  17. 97
    Code

    Code by Baoqiang Chen (21099509)

    Published 2025
    “…We divided the dataset into training and test sets, using 70% of the genes for training and 30% for testing. We implemented machine learning algorithms using the following R packages: rpart for Decision Trees, gbm for Gradient Boosting Machines (GBM), ranger for Random Forests, the glm function for Generalized Linear Models (GLM), and xgboost for Extreme Gradient Boosting (XGB). …”
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  18. 98
    RNA Velocity model simulation results. (A) Simulation results for RNA Velocity model without delay ( samples). The simulation is composed of an up-regulation phase (both production and degradation) and a down-regulation phase (degradation-only). In the first phase, <i>%</i>, <i>%</i> and <i>%</i> for the reaction time . In the second phase,  ∕ 1, <i>X</i> ∼ NB ( <i>r</i> , <i>μ</i> )  and <i>μ</i>. (B) Simulation results for RNA Velocity model with delay in degradation ( samples). RNA degradation is specified as a delayed reaction. In the first (up-regulation) phase: <i>Y</i> ≡ <i>n</i>, <i>t</i> = 0 and <i>%</i>. In the second (down-regulation) phase: <i>%</i>, <i>%</i> and <i>%.</i>

    RNA Velocity model simulation results. (A) Simulation results for RNA Velocity model without delay ( samples). The simulation is composed of an up-regulation phase (both production... by Ziyan Jin (21021976)

    Published 2025
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  19. 99
    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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  20. 100
    DeepB3Pred_source_code

    DeepB3Pred_source_code by Muhammad Arif (17012472)

    Published 2025
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