Combined Analysis of Network Toxicology and Multiomics Revealed the Potential Mechanism of

A 126.7 KB dataset from a 2026 study by Yang Li, integrating network toxicology, transcriptomics, metabolomics, and animal experiments. It provides data on the potential mechanism of liver injury in F1 male mouse offspring exposed to the fungicide Mancozeb. The dataset includes results from molecular docking and dynamics simulations, identifying core protein targets and disrupted pathways.

Use Cases

• Validate predicted protein targets like AKT1 and TP53 based on network toxicology analysis.
• Analyze disrupted metabolic pathways, such as steroid hormone biosynthesis, using integrated multiomics data.
• Benchmark molecular docking and dynamics simulation results for fungicide-protein interactions.
• Investigate transcriptomic and metabolomic signatures associated with developmental exposure hepatotoxicity.

Strengths

• Integrates four distinct analytical methods: network toxicology, transcriptomics, metabolomics, and animal experiments.
• Includes results from molecular docking and molecular dynamics simulations for validation.
• Dataset is small (126.7 KB), facilitating quick download and initial inspection.

Limitations

• Row count and column definitions are unknown, limiting suitability assessment.
• Column-level documentation is absent; field semantics must be inferred after download.
• Data is from a single study on mice, which may limit generalizability.

Provenance

Source

Yang Li via figshare

Collection Method

Generated from an integrated experimental study involving network analysis, multiomics, and animal models.

Freshness

Last updated 2026-03-18 12:36:33.