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

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

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Algorithm verification. by Dmitrii Smirnov (8822324)

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

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

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

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

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Algorithm for generating hyperparameter. by Balraj Preet Kaur (20370832)

“…Motivated by the above, in this proposal, we design an improved model to predict the existence of respiratory disease among patients by incorporating hyperparameter optimization and feature selection. To optimize the parameters of the machine learning algorithms, hyperparameter optimization with a genetic algorithm is proposed and to reduce the size of the feature set, feature selection is performed using binary grey wolf optimization algorithm. …”

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

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

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

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Results of machine learning algorithm. by Balraj Preet Kaur (20370832)

“…Motivated by the above, in this proposal, we design an improved model to predict the existence of respiratory disease among patients by incorporating hyperparameter optimization and feature selection. To optimize the parameters of the machine learning algorithms, hyperparameter optimization with a genetic algorithm is proposed and to reduce the size of the feature set, feature selection is performed using binary grey wolf optimization algorithm. …”

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

ROC comparison of machine learning algorithm. by Balraj Preet Kaur (20370832)

“…Motivated by the above, in this proposal, we design an improved model to predict the existence of respiratory disease among patients by incorporating hyperparameter optimization and feature selection. To optimize the parameters of the machine learning algorithms, hyperparameter optimization with a genetic algorithm is proposed and to reduce the size of the feature set, feature selection is performed using binary grey wolf optimization algorithm. …”

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

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

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

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)

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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)

“…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). …”

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)

“…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). …”