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Showing 1 - 20 results of 551 for search '(( binary based reaction optimization algorithm ) OR ( genes based model optimization algorithm ))', query time: 0.72s Refine Results
  1. 1
    <i>Patient 1</i> GA-optimized model error.

    <i>Patient 1</i> GA-optimized model error. by Dmitrii Smirnov (8822324)

    Published 2020
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  2. 2
    <i>Patient 8</i> GA-optimized model error.

    <i>Patient 8</i> GA-optimized model error. by Dmitrii Smirnov (8822324)

    Published 2020
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  3. 3
    Optimal Drug-Target Docking Visualization.

    Optimal Drug-Target Docking Visualization. by Qijun Wo (12992781)

    Published 2025
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  4. 4
    Algorithm verification.

    Algorithm verification. by Dmitrii Smirnov (8822324)

    Published 2020
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  5. 5
    Genetic Algorithm (GA) and CAGE-based personalization block diagrams.

    Genetic Algorithm (GA) and CAGE-based personalization block diagrams. by Dmitrii Smirnov (8822324)

    Published 2020
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  6. 6
    Screening MMRG gene variables associated with UC based on the optimal machine learning model.

    Screening MMRG gene variables associated with UC based on the optimal machine learning model. by Li Li (14993)

    Published 2025
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  7. 7
    <i>Patient 2</i> CAGE-based model error.

    <i>Patient 2</i> CAGE-based model error. by Dmitrii Smirnov (8822324)

    Published 2020
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  8. 8
    <i>Patient 9–11</i> RNA-seq based models error.

    <i>Patient 9–11</i> RNA-seq based models error. by Dmitrii Smirnov (8822324)

    Published 2020
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  9. 9
    Comparison of presented algorithm precision to Bot et al.

    Comparison of presented algorithm precision to Bot et al. by Dmitrii Smirnov (8822324)

    Published 2020
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  10. 10
    Model convergence to steady state.

    Model convergence to steady state. by Dmitrii Smirnov (8822324)

    Published 2020
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  11. 11
    Key gene identification based on two machine learning algorithms.

    Key gene identification based on two machine learning algorithms. by Lijun Guo (605276)

    Published 2025
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  12. 12
    Distributions of model parameters mirror differentially expressed ion channel genes.

    Distributions of model parameters mirror differentially expressed ion channel genes. by Daniele Linaro (227790)

    Published 2022
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  13. 13
    <i>Patient 8</i> GA output model.

    <i>Patient 8</i> GA output model. by Dmitrii Smirnov (8822324)

    Published 2020
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  14. 14
    The flow chart of harmony search-based OED using a double nested loops.

    The flow chart of harmony search-based OED using a double nested loops. by Wei Zhang (405)

    Published 2019
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  15. 15
    Table 1_A computational framework for optimizing mRNA vaccine delivery via AI-guided nanoparticle design and in silico gene expression profiling.pdf

    Table 1_A computational framework for optimizing mRNA vaccine delivery via AI-guided nanoparticle design and in silico gene expression profiling.pdf by Valentina Di Salvatore (6296114)

    Published 2025
    “…Parallelly, we trained a Random Forest regression model on simulated lipid nanoparticles formulations to predict immune activation values and embedded this model into a genetic algorithm to identify optimal lipid nanoparticles design parameters (size, charge, polyethylene glycol content, and targeting). …”
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  16. 16
    Presentation 1_A computational framework for optimizing mRNA vaccine delivery via AI-guided nanoparticle design and in silico gene expression profiling.pdf

    Presentation 1_A computational framework for optimizing mRNA vaccine delivery via AI-guided nanoparticle design and in silico gene expression profiling.pdf by Valentina Di Salvatore (6296114)

    Published 2025
    “…Parallelly, we trained a Random Forest regression model on simulated lipid nanoparticles formulations to predict immune activation values and embedded this model into a genetic algorithm to identify optimal lipid nanoparticles design parameters (size, charge, polyethylene glycol content, and targeting). …”
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  17. 17
    Table 2_A computational framework for optimizing mRNA vaccine delivery via AI-guided nanoparticle design and in silico gene expression profiling.pdf

    Table 2_A computational framework for optimizing mRNA vaccine delivery via AI-guided nanoparticle design and in silico gene expression profiling.pdf by Valentina Di Salvatore (6296114)

    Published 2025
    “…Parallelly, we trained a Random Forest regression model on simulated lipid nanoparticles formulations to predict immune activation values and embedded this model into a genetic algorithm to identify optimal lipid nanoparticles design parameters (size, charge, polyethylene glycol content, and targeting). …”
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  18. 18
    Image 1_A computational framework for optimizing mRNA vaccine delivery via AI-guided nanoparticle design and in silico gene expression profiling.png

    Image 1_A computational framework for optimizing mRNA vaccine delivery via AI-guided nanoparticle design and in silico gene expression profiling.png by Valentina Di Salvatore (6296114)

    Published 2025
    “…Parallelly, we trained a Random Forest regression model on simulated lipid nanoparticles formulations to predict immune activation values and embedded this model into a genetic algorithm to identify optimal lipid nanoparticles design parameters (size, charge, polyethylene glycol content, and targeting). …”
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  19. 19
    Image 2_A computational framework for optimizing mRNA vaccine delivery via AI-guided nanoparticle design and in silico gene expression profiling.png

    Image 2_A computational framework for optimizing mRNA vaccine delivery via AI-guided nanoparticle design and in silico gene expression profiling.png by Valentina Di Salvatore (6296114)

    Published 2025
    “…Parallelly, we trained a Random Forest regression model on simulated lipid nanoparticles formulations to predict immune activation values and embedded this model into a genetic algorithm to identify optimal lipid nanoparticles design parameters (size, charge, polyethylene glycol content, and targeting). …”
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  20. 20
    Parameter values (relative to baseline O’Hara-Rudy model [12]) of <i>Patient 9</i> models derived via GA or via comparison of mRNA levels to reference patient.

    Parameter values (relative to baseline O’Hara-Rudy model [12]) of <i>Patient 9</i> models derived via GA or via comparison of mRNA levels to reference patient. by Dmitrii Smirnov (8822324)

    Published 2020
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