FangNet

FangNet extracts and analyzes hidden knowledge from traditional Chinese medicine (TCM) clinical empirical prescriptions using network pharmacology and bioinformatics to identify key herbs and their symptom associations.

Key Features:

• Network-based Herb Ranking: Applies a structure network algorithm based on PageRank to rank herbs by topological importance within symptom-herb networks derived from clinical empirical prescriptions.
• Herb-Herb Co-occurrence Analysis: Detects and quantifies patterns of co-occurrence among herbs across clinical formulas to reveal frequently paired herbs and potential synergistic combinations.
• Symptom-Herb Association Mapping: Maps herbs to specific symptoms from clinical prescriptions to characterize therapeutic roles and associations within the symptom-herb network.
• Symptom-Herb Repository: Constructs a centralized collection of symptom-herb connections to support identification of biologically active ingredients and exploration of therapeutic hypotheses.
• Network Pharmacology and Bioinformatics Integration: Integrates network pharmacology and bioinformatics analyses to interpret complex herbal combinations and prioritize herbs for further study.

Scientific Applications:

• TCM Formula Mining: Systematically analyzes clinical empirical prescriptions to identify core herbal components within traditional Chinese medicine formulas.
• Active Ingredient Discovery: Prioritizes herbs based on network-derived importance to guide targeted isolation and characterization of biologically active compounds.
• Symptom-to-Herb Translation: Maps symptom associations to herbs to support research into symptom-targeted therapeutic strategies and multi-component treatments for complex diseases.

Methodology:

FangNet constructs a symptom-herb network from clinical empirical prescriptions and applies the PageRank algorithm to quantify each herb's relative importance and to reveal herb co-occurrence patterns and associations with specific symptoms.