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Showing 41 - 60 results of 268 for search '(( python pre implementation ) OR ( ((python model) OR (python tool)) implementation ))', query time: 0.35s Refine Results
  1. 41
    Configurations used in ALAAMs for undirected networks.

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

    Published 2024
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  2. 42
    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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  3. 43
    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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  4. 44
    Configurations used in ALAAMs for directed networks.

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

    Published 2024
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  5. 45
    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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  6. 46
    Example ALAAMEE output.

    Example ALAAMEE output. by Alex Stivala (8356257)

    Published 2024
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  7. 47
    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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  8. 48
    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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  9. 49
    Network descriptive statistics for the Deezer networks.

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

    Published 2024
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  10. 50
    Example ALAAMEE goodness-of-fit output.

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

    Published 2024
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  11. 51
    Global Aridity Index and Potential Evapotranspiration Climate Database v3.1 - Algorithm Code (Python)

    Global Aridity Index and Potential Evapotranspiration Climate Database v3.1 - Algorithm Code (Python) by Robert Zomer (12796235)

    Published 2025
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  12. 52
    Workflow of segmentation model creation and application.

    Workflow of segmentation model creation and application. by Vincent Hickl (16947429)

    Published 2025
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  13. 53
    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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  14. 54
    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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  15. 55
    Five Operator Lattice Simulation

    Five Operator Lattice Simulation by James McDaniel (22522571)

    Published 2025
    “…<p dir="ltr">This dataset contains the Python simulation code and supporting documentation for the paper <i>A Five-Operator Lattice of Consciousness: A Logical Framework for Mediation Between Implicit and Explicit Processing</i> (McDaniel, 2025).…”
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  16. 56
    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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  17. 57
    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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  18. 58
    DataSheet1_Prostruc: an open-source tool for 3D structure prediction using homology modeling.PDF

    DataSheet1_Prostruc: an open-source tool for 3D structure prediction using homology modeling.PDF by Shivani V. Pawar (20355171)

    Published 2024
    “…</p>Methods<p>Prostruc is a Python-based homology modeling tool designed to simplify protein structure prediction through an intuitive, automated pipeline. …”
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  19. 59
    DataSheet1_Prostruc: an open-source tool for 3D structure prediction using homology modeling.PDF

    DataSheet1_Prostruc: an open-source tool for 3D structure prediction using homology modeling.PDF by Shivani V. Pawar (20355171)

    Published 2024
    “…</p>Methods<p>Prostruc is a Python-based homology modeling tool designed to simplify protein structure prediction through an intuitive, automated pipeline. …”
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  20. 60
    BaNDyT: Bayesian Network Modeling of Molecular Dynamics Trajectories

    BaNDyT: Bayesian Network Modeling of Molecular Dynamics Trajectories by Elizaveta Mukhaleva (20602550)

    Published 2025
    “…We believe that BaNDyT is the first software package to include specialized and advanced features for analyzing MD simulation trajectories using a probabilistic graphical network model. We describe here the software’s uses, the methods associated with it, and a comprehensive Python interface to the underlying generalist BNM code. …”
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