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. …”

Pathway 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. …”

GO analysis bubble plot for other four modules. 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. …”

(A) Serial interval windows of COVID-19 for infector-infectee pairs [18]. by Oswaldo Gressani (13933254)

Simulation results of sudden reduction of load torque. by Haoran Cui (7235234)

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AP improvement rates for various targets on the RSOD dataset. by Pingping Yan (462509)

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Comparison of computational requirements for different control systems. by Haoran Cui (7235234)

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Comprehensive performance effects of three control systems under sudden load and sudden load reduction conditions. by Haoran Cui (7235234)

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Comparison results of three system related speeds. by Haoran Cui (7235234)

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Mathematical analysis process of sliding mode speed controller design based on traditional EAL. by Haoran Cui (7235234)

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Comparison results of phase trajectories and local enlarged images between two systems. by Haoran Cui (7235234)

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Comparison results of local Q-axis command current of ASMC and SMC systems under sudden load increase and anti-interference of three systems. by Haoran Cui (7235234)

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Speed response and torque response results when parameter <i>H</i>₀ changes. by Haoran Cui (7235234)

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