The speciation of ANEAT model evolution. by Wenbing Shi (5806160)

“…This paper proposes a CGO risk prediction method based on data augmentation and a neuroevolution algorithm, denoted as ANEAT. First, sample features are applied to the transfer function using a pointwise intensity transformation to obtain new feature samples. …”

The analysis of feature importance. by Wenbing Shi (5806160)

“…This paper proposes a CGO risk prediction method based on data augmentation and a neuroevolution algorithm, denoted as ANEAT. First, sample features are applied to the transfer function using a pointwise intensity transformation to obtain new feature samples. …”

S1 Data - by Wenbing Shi (5806160)

“…This paper proposes a CGO risk prediction method based on data augmentation and a neuroevolution algorithm, denoted as ANEAT. First, sample features are applied to the transfer function using a pointwise intensity transformation to obtain new feature samples. …”

The fitness of ANEAT model evolution. by Wenbing Shi (5806160)

“…This paper proposes a CGO risk prediction method based on data augmentation and a neuroevolution algorithm, denoted as ANEAT. First, sample features are applied to the transfer function using a pointwise intensity transformation to obtain new feature samples. …”

The structure of the data sample. by Wenbing Shi (5806160)

“…This paper proposes a CGO risk prediction method based on data augmentation and a neuroevolution algorithm, denoted as ANEAT. First, sample features are applied to the transfer function using a pointwise intensity transformation to obtain new feature samples. …”

The genome recombination of neuroevolution. by Wenbing Shi (5806160)

“…This paper proposes a CGO risk prediction method based on data augmentation and a neuroevolution algorithm, denoted as ANEAT. First, sample features are applied to the transfer function using a pointwise intensity transformation to obtain new feature samples. …”

The principle of sample data augmentation. by Wenbing Shi (5806160)

“…This paper proposes a CGO risk prediction method based on data augmentation and a neuroevolution algorithm, denoted as ANEAT. First, sample features are applied to the transfer function using a pointwise intensity transformation to obtain new feature samples. …”

The fitness of NANEAT model evolution. by Wenbing Shi (5806160)

“…This paper proposes a CGO risk prediction method based on data augmentation and a neuroevolution algorithm, denoted as ANEAT. First, sample features are applied to the transfer function using a pointwise intensity transformation to obtain new feature samples. …”

The speciation of NANEAT model evolution. by Wenbing Shi (5806160)

“…This paper proposes a CGO risk prediction method based on data augmentation and a neuroevolution algorithm, denoted as ANEAT. First, sample features are applied to the transfer function using a pointwise intensity transformation to obtain new feature samples. …”

A novel cost-palatability bi-objective approach to the menu planning problem with an innovative similarity metric using a path relinking algorithm by F. Martos-Barrachina (18142537)

“…For this, a novel Similarity Function is introduced, which evaluates the proximity of two different menus and returns a similarity metric between 0 and 1. …”

Integration of Morphoscanner with MDAnalysis, NGLview and Networkx. by Federico Fontana (533670)

Merged dataset with analysis of variance and visualization after removal of batch effects for GSE183272 and GSE236374. by Wenlong Kuang (20485167)

Translatomic analysis of GLAST-Cre⁺ cells. by Tomokatsu Udagawa (11675636)

Data_Sheet_1_Integrated Bioinformatics Algorithms and Experimental Validation to Explore Robust Biomarkers and Landscape of Immune Cell Infiltration in Dilated Cardiomyopathy.ZIP by Qingquan Zhang (1405300)

“…In addition, the differentially expressed genes (DEGs) were screened by the limma package, and DEGs were analyzed for functional enrichment. In the protein–protein interaction (PPI) network, multiple algorithms were used to calculate the score of each DEG for screening the hub genes. …”

DataSheet2_Classification and biomarker gene selection of pyroptosis-related gene expression in psoriasis using a random forest algorithm.CSV by Jian-Kun Song (11711756)

“…A principal component analysis (PCA) was conducted to determine whether PRGs could be used to distinguish the samples. …”

DataSheet3_Classification and biomarker gene selection of pyroptosis-related gene expression in psoriasis using a random forest algorithm.CSV by Jian-Kun Song (11711756)

“…A principal component analysis (PCA) was conducted to determine whether PRGs could be used to distinguish the samples. …”

DataSheet1_Classification and biomarker gene selection of pyroptosis-related gene expression in psoriasis using a random forest algorithm.pdf by Jian-Kun Song (11711756)

“…A principal component analysis (PCA) was conducted to determine whether PRGs could be used to distinguish the samples. …”

Flow diagram of the data-driven optimal control approach. by Ritabrata Dutta (11268329)

Where to Handle an Exception? Recommending Exception Handling Locations from a Global Perspective by Xiangyang Jia (10026932)

Fig 6 - by Parthiban Aravamudhan (15338781)