Drug target genes and molecular mechanism investigation in isoflurane-induced anesthesia based on WGCNA and machine learning methods

Honglei Yuana Department of Anesthesiology, Taian City Central Hospital, Taian, Shandong, China

Shengqiang Yanga Department of Anesthesiology, Taian City Central Hospital, Taian, Shandong, China

Peng Hana Department of Anesthesiology, Taian City Central Hospital, Taian, Shandong, China

Mingya Sunb Taian City Taishan District Dai Temple Community Health Service Center, Taian, Shandong, China

Chao Zhoua Department of Anesthesiology, Taian City Central Hospital, Taian, Shandong, ChinaCorrespondence[email protected]

Supplemental material

Supplementary Figure 4. The gene set enrichment analysis (GSEA) based on four drug targets.

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Supplementary Figure 3. Four drug targets revealed by VENN plot based on Least absolute shrinkage and selection operator (LASSO) regression, Support Vector Machine (SVM) analysis and randomForest (RF) algorithm.

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Supplementary Figure 2. Significant GO function for modules in PPI network

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Supplementary Figure 1. The results of clustering analysis for samples in GSE64617.

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Drug target genes and molecular mechanism investigation in isoflurane-induced anesthesia based on WGCNA and machine learning methods

Supplementary Figure 4. The gene set enrichment analysis (GSEA) based on four drug targets.

Supplementary Figure 3. Four drug targets revealed by VENN plot based on Least absolute shrinkage and selection operator (LASSO) regression, Support Vector Machine (SVM) analysis and randomForest (RF) algorithm.

Supplementary Figure 2. Significant GO function for modules in PPI network

Supplementary Figure 1. The results of clustering analysis for samples in GSE64617.

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Drug target genes and molecular mechanism investigation in isoflurane-induced anesthesia based on WGCNA and machine learning methods

Supplementary Figure 4. The gene set enrichment analysis (GSEA) based on four drug targets.

Supplementary Figure 3. Four drug targets revealed by VENN plot based on Least absolute shrinkage and selection operator (LASSO) regression, Support Vector Machine (SVM) analysis and randomForest (RF) algorithm.

Supplementary Figure 2. Significant GO function for modules in PPI network

Supplementary Figure 1. The results of clustering analysis for samples in GSE64617.

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