WGCNA was employed to identify modules associated with the synthetic periodontitis dataset.

WGCNA was employed to identify modules associated with the synthetic periodontitis dataset.

<p>(A, B) Based on scale independence and average connectivity, β = 8 was considered the optimal soft-thresholding value. (C) Gene clustering tree with multiple partitioned modules. Different clusters are connected by different colors. (D) Heatmap of adjacency of feature genes.</p>

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Bibliographic Details
Main Author: Zhifeng Wu (303016) (author)
Other Authors: Fan Zhang (46132) (author), Yuan Wang (14955) (author), Chunjiang Liu (125011) (author), Zhaokun Sun (19197309) (author), Xiaoqi Tang (12996656) (author), Liming Tang (2231494) (author)
Published: 2025
Subjects:
Biochemistry
Microbiology
Cell Biology
Genetics
Molecular Biology
Immunology
Biological Sciences not elsewhere classified
Information Systems not elsewhere classified
targeted drug prediction
receiver operating characteristic
conducted differential analysis
abdominal aortic aneurysm
expression network analysis
external independent datasets
ppi network analysis
related module genes
identified key genes
ppi network
candidate genes
obtained datasets
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validation confirmed
ten drugs
robustly associated
positive correlations
neutrophil infiltration
machine learning
intersection initially
geo database
bioinformatics approaches
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Summary:<p>(A, B) Based on scale independence and average connectivity, β = 8 was considered the optimal soft-thresholding value. (C) Gene clustering tree with multiple partitioned modules. Different clusters are connected by different colors. (D) Heatmap of adjacency of feature genes.</p>

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