Dynamics of proposed method when a hyperbola and circle intersect, the number of iterations are ten and tolerance is 0.01. by Salima Kouser (21416698)

Dynamics of proposed method when a parabola and circle intersect, the number of iterations are ten and tolerance is 0.01. by Salima Kouser (21416698)

Dynamics of proposed method when a circle and circle intersect, the number of iterations are ten and tolerance is 0.01. by Salima Kouser (21416698)

pone.0317752.t005 - Towards efficient solutions: A novel approach to quadratic nonlinearity in boundary value problems by Salima Kouser (21416698)

Dynamics of proposed method when a line and circle intersect, the number of iterations are ten and tolerance is 0.01. by Salima Kouser (21416698)

2D density plot of maximum G4Hunter scores for G4s whose formation propensity is computationally impaired by aSNPs. by Rongxin Zhang (1618159)

Average Friedman test ranks (left) and pairwise average Wilcoxon signed-rank test p-values (right) for all compared optimization algorithms across CL, C17, C19, C21, C22, and RWPs. by Mahmood A. Jumaah (22272371)

A synopsis of the research design and flowchart. by Ravinder Sharma (13154079)

“…Subsequently, using the MCODE algorithm, we identified 6 hub genes—ATN1, JPH3, TBP, VPS13A, DMD, and HTT—as core candidates. …”

Trajectory comparison chart: (A) starting point 1; (B) starting point 2; (C) starting point 3. by Jingnan Yan (20727331)

Line chart plotted using the elbow method: (A) system 1; (B) system 2; (C) system 3. by Jingnan Yan (20727331)

KEGG analysis bubble plot for other four module. by Zongjin Li (38031)

“…This algorithm utilizes a graph network to represent the interaction information between genes, builds a GNN model, designs a loss function based on link prediction and self-supervised learning ideas for training, and allows each gene node to obtain a feature vector representing global information. …”

Visualization for the module 1. by Zongjin Li (38031)

“…This algorithm utilizes a graph network to represent the interaction information between genes, builds a GNN model, designs a loss function based on link prediction and self-supervised learning ideas for training, and allows each gene node to obtain a feature vector representing global information. …”

Significance levels of gene biomarkers. by Zongjin Li (38031)

“…This algorithm utilizes a graph network to represent the interaction information between genes, builds a GNN model, designs a loss function based on link prediction and self-supervised learning ideas for training, and allows each gene node to obtain a feature vector representing global information. …”

Clustering performance comparison. by Zongjin Li (38031)

“…This algorithm utilizes a graph network to represent the interaction information between genes, builds a GNN model, designs a loss function based on link prediction and self-supervised learning ideas for training, and allows each gene node to obtain a feature vector representing global information. …”

ROC for all gene biomarkers as features. by Zongjin Li (38031)

“…This algorithm utilizes a graph network to represent the interaction information between genes, builds a GNN model, designs a loss function based on link prediction and self-supervised learning ideas for training, and allows each gene node to obtain a feature vector representing global information. …”

The gene biomarkers by the proposed method. by Zongjin Li (38031)

“…This algorithm utilizes a graph network to represent the interaction information between genes, builds a GNN model, designs a loss function based on link prediction and self-supervised learning ideas for training, and allows each gene node to obtain a feature vector representing global information. …”

Heat map analysis of the biomarkers. by Zongjin Li (38031)

“…This algorithm utilizes a graph network to represent the interaction information between genes, builds a GNN model, designs a loss function based on link prediction and self-supervised learning ideas for training, and allows each gene node to obtain a feature vector representing global information. …”

ROC curve for gene biomarkers. by Zongjin Li (38031)

“…This algorithm utilizes a graph network to represent the interaction information between genes, builds a GNN model, designs a loss function based on link prediction and self-supervised learning ideas for training, and allows each gene node to obtain a feature vector representing global information. …”

Comparison with published methods. by Zongjin Li (38031)

“…This algorithm utilizes a graph network to represent the interaction information between genes, builds a GNN model, designs a loss function based on link prediction and self-supervised learning ideas for training, and allows each gene node to obtain a feature vector representing global information. …”

GO analysis bubble plot for module 1. by Zongjin Li (38031)

“…This algorithm utilizes a graph network to represent the interaction information between genes, builds a GNN model, designs a loss function based on link prediction and self-supervised learning ideas for training, and allows each gene node to obtain a feature vector representing global information. …”