Table 2_A computational framework for optimizing mRNA vaccine delivery via AI-guided nanoparticle design and in silico gene expression profiling.pdf حسب 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). …"

Image 1_A computational framework for optimizing mRNA vaccine delivery via AI-guided nanoparticle design and in silico gene expression profiling.png حسب 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). …"

Image 2_A computational framework for optimizing mRNA vaccine delivery via AI-guided nanoparticle design and in silico gene expression profiling.png حسب 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). …"

IRBMO vs. meta-heuristic algorithms boxplot. حسب 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. …"

IRBMO vs. feature selection algorithm boxplot. حسب 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. …"

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. حسب Dmitrii Smirnov (8822324)

الموضوعات:

Initialization of variables in the APDCA algorithm. حسب Yangsong He (13909371)

الموضوعات:

Comparison of lower bounds in estimating kinetic parameters <i>k</i>_<i>i</i> obtained by HS-OED algorithm and GA,PSO-based OED methods. حسب Wei Zhang (405)

UpSet diagram illustrating the overlap of DNBs selected by our method and top-ranked genes by the 13 baseline centrality-based ranking algorithms. حسب Fatemeh Keikha (20917848)

Optimal image settings for object detection of Iba1 immunolabelled cells in DAB. حسب Danish M. Anwer (15383441)

<i>OptRAM</i>: <i>In-silico</i> strain design via integrative regulatory-metabolic network modeling حسب Fangzhou Shen (5140994)

"…To address challenges in metabolic engineering, computational strain optimization algorithms based on genome-scale metabolic models have increasingly been used to aid in overproducing products of interest. …"

Algorithm 2: Maintain inhibitory densities coherence. حسب Dimitri Rodarie (10148838)

Most informative genes for predicting cell-line responses for Gefitinib. حسب Sofia P. Miranda (11418930)

DataSheet1_Establishment and Optimization of Radiomics Algorithms for Prediction of KRAS Gene Mutation by Integration of NSCLC Gene Mutation Mutual Exclusion Information.DOCX حسب Jingyi Wang (550305)

"…<p>Purpose: To assess the significance of mutation mutual exclusion information in the optimization of radiomics algorithms for predicting gene mutation.…"

Unsupervised clustering and marker gene signatures. حسب Josh L. Espinoza (10492448)

Expression values and standard errors of genes within each patient group under the optimal feature filtering method (ALOGA < 128) used for (blue bars) distinguishing each individua... حسب Song Cui (553627)

Subtype classification of AMI patients based on hub genes. حسب Nan Huang (464390)

Gaussian noise. حسب Dmitrii Smirnov (8822324)

الموضوعات:

Convergence without crossover. حسب Dmitrii Smirnov (8822324)

الموضوعات:

Heterogeneity of APD for Patient 1 and Patient 2. حسب Dmitrii Smirnov (8822324)

الموضوعات: