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Showing 81 - 100 results of 297 for search '(( python code implementing ) OR ( ((python model) OR (python tool)) implemented ))', query time: 0.41s Refine Results
  1. 81
    Network descriptive statistics for the Deezer networks.

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

    Published 2024
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  2. 82
    Example ALAAMEE goodness-of-fit output.

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

    Published 2024
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  3. 83
    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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  4. 84
    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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  5. 85
    DeepB3Pred - code

    DeepB3Pred - code by Saleh Musleh (22263718)

    Published 2025
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  6. 86
    Workflow of segmentation model creation and application.

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

    Published 2025
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  7. 87
    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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  8. 88
    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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  9. 89
    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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  10. 90
    DeepB3Pred_source_code

    DeepB3Pred_source_code by Muhammad Arif (17012472)

    Published 2025
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  11. 91
    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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  12. 92
    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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  13. 93
    Scripts, data and figures underpinning 'Towards the Creation of Legible Octilinear Power Grid Diagrams Using Mixed Integer Linear Programming'

    Scripts, data and figures underpinning 'Towards the Creation of Legible Octilinear Power Grid Diagrams Using Mixed Integer Linear Programming' by Paul Cuffe (2937081)

    Published 2024
    “…<p dir="ltr">These Python notebooks implement the techniques described in the paper "Towards the Creation of Legible Octilinear Power Grid Diagrams Using Mixed Integer Linear Programming".…”
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  14. 94
    Probabilities of normal and various cyber attack types over time sequence.

    Probabilities of normal and various cyber attack types over time sequence. by Mohanad Faeq Ali (21354273)

    Published 2025
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  15. 95
    Graphical abstract of HCAP.

    Graphical abstract of HCAP. by Mohanad Faeq Ali (21354273)

    Published 2025
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  16. 96
    Recall analysis.

    Recall analysis. by Mohanad Faeq Ali (21354273)

    Published 2025
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  17. 97
    FPR and FNR analysis based on varying cyber attack types.

    FPR and FNR analysis based on varying cyber attack types. by Mohanad Faeq Ali (21354273)

    Published 2025
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  18. 98
    Analysis of IoMT data sources.

    Analysis of IoMT data sources. by Mohanad Faeq Ali (21354273)

    Published 2025
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  19. 99
    Prediction accuracy on varying attack types.

    Prediction accuracy on varying attack types. by Mohanad Faeq Ali (21354273)

    Published 2025
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  20. 100
    <b> </b> Precision analysis.

    <b> </b> Precision analysis. by Mohanad Faeq Ali (21354273)

    Published 2025
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