Network Pharmacology Study of Shuangjiang Decoction and Labetalol for Preeclampsia

83 overlapping drug targets and ten hub genes (IL1A, IFNG, BCL2, REN, XDH, NOS3, NOS2, EGFR, MMP3, CCK) were identified in this integrative analysis of preeclampsia treatment. The dataset, created by Li Chen and shared under a CC-BY-4.0 license, integrates GEO data with network pharmacology and machine learning approaches. It was last updated on 2026-05-18.

Use Cases

• Validate potential biomarkers for preeclampsia based on the identified hub genes IFNG, MMP3, NOS2, NOS3, and REN.
• Investigate molecular pathways like HIF-1 signaling and estrogen signaling implicated in the therapeutic mechanism.
• Perform molecular docking simulations using the identified active ingredients Rhein, Baicalein, and Tanshinone IIA.
• Build diagnostic models for preeclampsia using machine learning on the highlighted gene targets.

Strengths

• Identifies 83 specific drug targets and 10 hub genes, providing a concrete molecular basis for analysis.
• Machine learning validation achieved an average ROC-AUC of 0.82 for a diagnostic model.
• Molecular docking results confirm strong binding affinities for key active ingredients.

Limitations

• Row count is unknown, which may limit suitability assessment.
• Column-level documentation is absent; field semantics must be inferred after download.
• The dataset is small at 30.9 KB, indicating limited scope.

Provenance

Source

figshare

Collection Method

Integrated analysis of GEO datasets with network pharmacology and machine learning.

Freshness

Last updated 2026-05-18 09:40:03; freshness should be verified.