Identification of Endoplasmic Reticulum Stress-Related Biomarkers of Periodontitis Based on Machine Learning: A Bioinformatics Analysis

<div>Identification of DEGs of periodontitis. (a) PCA plot of diseased and healthy periodontal tissue samples after the batch effect between GSE10334, GES16134, and GES1613 was removed. (b) Volcano plot of DEGs in training the set; green represented downregulated DEGs, black represented genes with no significant difference, and red represented upregulated DEGs. (c) Heat map of 36 DEGs with significant differences in expression between diseased and healthy periodontal samples.</div>

Disease Markers

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Figure 2

Figure 2: Identification of Endoplasmic Reticulum Stress-Related Biomarkers of Periodontitis Based on Machine Learning: A Bioinformatics Analysis 