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

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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Generalized Low-Rank Plus Sparse Tensor Estimation by Fast Riemannian Optimization by Jian-Feng Cai (498405)

Binary contingency table. by Lu Xiao (314030)

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

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

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

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

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Overview of the segmentation algorithm. by Michael C. Robitaille (11949174)

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

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

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

<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)

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). …”

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)

“…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). …”