Table_1_A scheduling route planning algorithm based on the dynamic genetic algorithm with ant colony binary iterative optimization for unmanned aerial vehicle spraying in multiple tea fields.XLSX by Yangyang Liu (807797)

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

DataSheet_1_Comparative analysis of assembly algorithms to optimize biosynthetic gene cluster identification in novel marine actinomycete genomes.docx by Daniela Tizabi (10297654)

DataSheet_2_Comparative analysis of assembly algorithms to optimize biosynthetic gene cluster identification in novel marine actinomycete genomes.pdf by Daniela Tizabi (10297654)

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

Graphical illustration of sample size estimation for the study protocol. by Müge Yalçin (16486563)

Subjects:

Study design flow chart. by Müge Yalçin (16486563)

Subjects:

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)

“…Differential gene expression analysis identified compartment-specific transcriptional responses, which were then used to construct a risk index based on predicted immune activation and the number of upregulated immune markers. …”

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)

“…Differential gene expression analysis identified compartment-specific transcriptional responses, which were then used to construct a risk index based on predicted immune activation and the number of upregulated immune markers. …”

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)

“…Differential gene expression analysis identified compartment-specific transcriptional responses, which were then used to construct a risk index based on predicted immune activation and the number of upregulated immune markers. …”

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)

“…Differential gene expression analysis identified compartment-specific transcriptional responses, which were then used to construct a risk index based on predicted immune activation and the number of upregulated immune markers. …”

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)

“…Differential gene expression analysis identified compartment-specific transcriptional responses, which were then used to construct a risk index based on predicted immune activation and the number of upregulated immune markers. …”

MultiCRISPR-EGA: Optimizing Guide RNA Array Design for Multiplexed CRISPR Using the Elitist Genetic Algorithm by Yangyu Zhang (4609117)

“…Recognizing that more stable gRNAs, characterized by lower minimum free energy (MFE), have prolonged activity and thus higher efficacy, we developed MultiCRISPR-EGA, a graphical user interface (GUI)-based tool that employs the Elitist Genetic Algorithm (EGA) to design optimized single-promoter-driven multiplexed gRNA arrays. …”

Stochastic simulation and statistical inference platform for visualization and estimation of transcriptional kinetics by Gennady Gorin (8466255)

“…As a demonstration, we show that the optimization algorithm can successfully recover the transcriptional kinetics of simulated and experimental gene expression data. …”

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. …”

Parameter settings of the comparison algorithms. by Ying Li (38224)

“…<div><p>Feature selection is an important solution for dealing with high-dimensional data in the fields of machine learning and data mining. In this paper, we present an improved mountain gazelle optimizer (IMGO) based on the newly proposed mountain gazelle optimizer (MGO) and design a binary version of IMGO (BIMGO) to solve the feature selection problem for medical data. …”

Datasets and their properties. by Olaide N. Oyelade (14047002)

“…<div><p>Feature selection problem represents the field of study that requires approximate algorithms to identify discriminative and optimally combined features. …”

Parameter settings. by Olaide N. Oyelade (14047002)

“…<div><p>Feature selection problem represents the field of study that requires approximate algorithms to identify discriminative and optimally combined features. …”

IRBMO vs. meta-heuristic algorithms boxplot. 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. …”

IRBMO vs. feature selection algorithm boxplot. 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. …”