Geranium Wilfordii Anti-Asthma Mechanisms via Network Pharmacology and Machine Learning

Yingxiang Wu's research article details a multifaceted study identifying 43 compounds in Geranium wilfordii Maxim. and prioritizing hub genes NOTCH2, HDAC2, and MAPK1 for asthma treatment. The work integrates network pharmacology, machine learning, Mendelian randomization, and experimental validation to explore immune cell modulation.

Use Cases

• Validate prioritized hub genes NOTCH2, HDAC2, and MAPK1 through molecular docking simulations
• Analyze the relationship between identified compounds and immune cell activity, such as CD4+ T cells and regulatory T cells
• Reproduce network pharmacology and machine learning workflows to identify potential therapeutic targets from compound lists
• Use Mendelian randomization analysis to assess causal relationships between hub genes and asthma phenotypes

Strengths

• Identifies 43 specific compounds from Geranium wilfordii Maxim. using UPLC-QE-Orbitrap-MS
• Prioritizes three key hub genes (NOTCH2, HDAC2, MAPK1) through integrated computational methods
• Validates findings through in vivo animal experiments demonstrating therapeutic efficacy

Limitations

• Dataset is a 13.0 MB DOCX document, not a structured, machine-readable data table
• No raw experimental data, gene expression matrices, or compound-target interaction tables are provided
• Findings are specific to the studied plant and asthma context, limiting generalizability

Provenance

Source

figshare, authored by Yingxiang Wu

Collection Method

Computational analysis (network pharmacology, machine learning, Mendelian randomization) combined with experimental validation (UPLC-QE-Orbitrap-MS, animal models)

Freshness

Last updated March 25, 2026.

Geography

Plant material associated with traditional use by minority groups in Southwest China