Identification and functional analysis of hub genes. by Wei Ya Lan (22403712)

“…(B) PPI network visualized using Cytoscape software. (C, D) Top 10 hub genes identified using the Maximal Clique Centrality (MCC) algorithm; darker colors indicate higher centrality within the PPI network. …”

Test functions. by Kejia Liu (5699651)

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Core genes were selected through PPI analysis based on three algorithms. by Dong-Hee Han (140305)

“…<b>(B)</b> The priority of the top 10 genes was evaluated through MNC, which identifies clusters of protein nodes that are more functionally connected to each other and selects the central proteins within the cluster. …”

Multimodal reference functions. by Ruiyu Zhan (21602031)

“…We performed comparative analyses against other methodologies across various functions and public datasets to assess their effectiveness. …”

The convergence curves of the test functions. by Ruiyu Zhan (21602031)

“…We performed comparative analyses against other methodologies across various functions and public datasets to assess their effectiveness. …”

Single-peaked reference functions. by Ruiyu Zhan (21602031)

“…We performed comparative analyses against other methodologies across various functions and public datasets to assess their effectiveness. …”

Fitness comparison on test function. by Kejia Liu (5699651)

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Test results of multimodal benchmark functions. by Ruiyu Zhan (21602031)

“…We performed comparative analyses against other methodologies across various functions and public datasets to assess their effectiveness. …”

Fixed-dimensional multimodal reference functions. by Ruiyu Zhan (21602031)

“…We performed comparative analyses against other methodologies across various functions and public datasets to assess their effectiveness. …”

Test results of multimodal benchmark functions. by Ruiyu Zhan (21602031)

“…We performed comparative analyses against other methodologies across various functions and public datasets to assess their effectiveness. …”

R-squared comparison of test function. by Kejia Liu (5699651)

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Differential gene analysis and functional enrichment of the GSE775 dataset over time using the ClusterGVis package. by Wenlong Kuang (20485167)

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Algorithm framework. by Zongjin Li (38031)

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GSEA Analysis of Differentially Expressed Genes in High and Low-Risk Groups. by Jie Chu (293795)

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GO/KEGG Enrichment Analysis of Differentially Expressed Genes in High and Low-Risk Groups. by Jie Chu (293795)

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Activation functions: Sigmoid function and ReLU function. by Wan-Hua Zhang (21601139)

Performance of ENQUIRE’s gene normalization algorithm. by Luca Musella (19986312)

Table 1_Comprehensive analysis of anoikis-related gene signature in ulcerative colitis using machine learning algorithms.xlsx by Peng Liu (120506)

“…Receiver operating characteristic (ROC) analysis was utilized to evaluate the diagnostic accuracy of each gene. Subsequently, Single sample GSEA (ssGSEA) was executed to explore the relationships within immune cell infiltration, UC subtypes, and key anoikis-DEGs. …”

Image 1_Comprehensive analysis of anoikis-related gene signature in ulcerative colitis using machine learning algorithms.tiff by Peng Liu (120506)

“…Receiver operating characteristic (ROC) analysis was utilized to evaluate the diagnostic accuracy of each gene. Subsequently, Single sample GSEA (ssGSEA) was executed to explore the relationships within immune cell infiltration, UC subtypes, and key anoikis-DEGs. …”

Image 4_Comprehensive analysis of anoikis-related gene signature in ulcerative colitis using machine learning algorithms.tiff by Peng Liu (120506)

“…Receiver operating characteristic (ROC) analysis was utilized to evaluate the diagnostic accuracy of each gene. Subsequently, Single sample GSEA (ssGSEA) was executed to explore the relationships within immune cell infiltration, UC subtypes, and key anoikis-DEGs. …”