Analyze TCM outcome associations, indications, and practice patterns.
Analyze how the model allocates attention when processing herbs contained in a formula and the symptoms the formula treats. Attention is not a direct measure of outcome association strength or causal effect of an herb on a symptom; it reflects which information the model prioritizes during inference. Higher attention indicates the reference value of specific information for understanding the current symptom or herb combination, or for distinguishing it from similar cases. Because reference contexts differ and the attention mechanism is dynamically adjusted for each specific input set, inconsistency between symptom→herb and herb→symptom cross-attention is a normal behavior consistent with model logic.
Visualize custom coordinate points in a 3D PCA space and compare their relative positions. Supports both manual input and file upload for multiple data types (symptoms, formulas, herbs, compounds, western drugs, and targets), with customizable point colors and labels for input items. Automatically computes all pairwise distances among input points and provides downloadable results. Symptom inputs support semantic encoding, so symptom names not present in the database can still be entered. It is recommended to use one consistent color per data type and avoid assigning the same label to multiple points to prevent confusion.
| 顺序编号 | Label | Type | Color | PC1 | PC2 | PC3 | Action |
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Based on clinical practice data, this module analyzes the relationship between coordinate distance and co-occurrence strength of symptom sets or herb sets in cases, to assess consistency between real-world clinical practice and the structure of the TCM embedding space. It supports providing symptom patterns and herb combinations in the same file with different counts, and they do not need one-to-one correspondence. Symptom inputs support semantic encoding, so symptom names not present in the database can still be entered. Both scatter plots and error-bar plots are supported; in the error-bar plot, point height is the mean and the upper/lower bar lengths are standard deviations.
Based on clinical outcome association data of TCM prescriptions, this module analyzes the relationship between outcome association indicators and coordinate distance, to evaluate how well the TCM embedding space explains outcome association variation. Symptom patterns and herb combinations in the data file must correspond one-to-one, along with an outcome association indicator. There are no restrictions on how the outcome association indicator is defined or its value range. Symptom inputs support semantic encoding, so symptom names not present in the database can still be entered. Both scatter plots and error-bar plots are supported; in the error-bar plot, point height is the mean and the upper/lower bar lengths are standard deviations.
Based on herb indication data, this module examines distribution differences in coordinate distances between indication-matched herb–symptom pairs and non-matched pairs. Symptom inputs support semantic encoding, so symptom names not present in the database can still be entered. Supports both box plots and violin plots.
Based on coordinates of disease symptom patterns in the TCM embedding space, this module builds a disease KNN network and annotates edges by whether diseases share symptoms and related genes (optional), helping discover potential shared mechanisms. Supports both manual input and file upload, and allows customization of the number of nearest neighbors. Multiple layouts are supported, including coordinate layout and four network layouts; in network layouts, point colors can represent coordinates. Provides downloads for both the disease list and edge list of the network.