Several loop patterns found in binary code. by Alba Pedro-Zapater (8600382)

<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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Proposed Algorithm. by Hend Bayoumi (22693738)

“…Hence, an Energy-Harvesting Reinforcement Learning-based Offloading Decision Algorithm (EHRL) is proposed. …”

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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Comparisons between ADAM and NADAM optimizers. by Hend Bayoumi (22693738)

“…Hence, an Energy-Harvesting Reinforcement Learning-based Offloading Decision Algorithm (EHRL) is proposed. …”

<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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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)

The Pseudo-Code of the IRBMO Algorithm. by Chenyi Zhu (9383370)

“…In order to comprehensively verify the performance of IRBMO, this paper designs a series of experiments to compare it with nine mainstream binary optimization algorithms. The experiments are based on 12 medical datasets, and the results show that IRBMO achieves optimal overall performance in key metrics such as fitness value, classification accuracy and specificity. …”

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). …”