<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>Modern experimental analysis techniques have the problem of inefficiency in the analysis of traditional Chinese medicine composition. For the construction of a knowledge graph of traditional Chinese medicine ingredients, this paper first proposes a two-way graph convolutional aggregation network model, which is characterized by the node features of the knowledge graph being enhanced by the relationship features in the initial embedding of entities and relationships. The performer model can reduce the computation of the attention score and improve the computational efficiency of the model. Next, the two-way graph convolutional aggregation network model is used to perform the analysis in the TCM-related database. Finally, the results of the analysis are applied to a real case study of traditional Chinese medicine composition analysis. The construction of the knowledge graph for traditional Chinese medicine ingredients resulted in the acquisition of 14 high-frequency keywords. In the results of the compositional analysis of the herbs of C. minor, 13 batches of samples could be clustered into three groups, and 11 common peaks and 4 common components were identified for the 13 batches of samples, which were protocatechuic acid, vanillic acid, caffeic acid, and chrysanthemic acid, respectively. A cumulative variance contribution of 84.853% was obtained after extracting three principal components.</p>
</abstract>

Modern experimental analysis techniques have the problem of inefficiency in the analysis of traditional Chinese medicine composition. For the construction of a knowledge graph of traditional Chinese medicine ingredients, this paper first proposes a two-way graph convolutional aggregation network model, which is characterized by the node features of the knowledge graph being enhanced by the relationship features in the initial embedding of entities and relationships. The performer model can reduce the computation of the attention score and improve the computational efficiency of the model. Next, the two-way graph convolutional aggregation network model is used to perform the analysis in the TCM-related database. Finally, the results of the analysis are applied to a real case study of traditional Chinese medicine composition analysis. The construction of the knowledge graph for traditional Chinese medicine ingredients resulted in the acquisition of 14 high-frequency keywords. In the results of the compositional analysis of the herbs of C. minor, 13 batches of samples could be clustered into three groups, and 11 common peaks and 4 common components were identified for the 13 batches of samples, which were protocatechuic acid, vanillic acid, caffeic acid, and chrysanthemic acid, respectively. A cumulative variance contribution of 84.853% was obtained after extracting three principal components.

Application and research on the method of analyzing the composition of traditional Chinese medicine based on knowledge mapping

Department of Pharmaceutical Sciences and Health Management

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

Modern experimental analysis techniques have the problem of inefficiency in the analysis of traditional Chinese medicine composition. For the construction of a...

Application and research on the method of analyzing the composition of traditional Chinese medicine based on knowledge mapping

Pubblicato online: 31 gen 2024

Pagine: -

Ricevuto: 20 dic 2023

Accettato: 26 dic 2023

DOI: https://doi.org/10.2478/amns-2024-0234

Parole chiave
Graph convolutional network, Knowledge graph, Herbal components, Cluster analysis, Principal component analysis

© 2024 Lin Wang, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Application and research on the method of analyzing the composition of traditional Chinese medicine based on knowledge mapping

Pubblicato online: 31 gen 2024

Pagine: -

Ricevuto: 20 dic 2023

Accettato: 26 dic 2023

DOI: https://doi.org/10.2478/amns-2024-0234

Parole chiaveGraph convolutional network, Knowledge graph, Herbal components, Cluster analysis, Principal component analysis

© 2024 Lin Wang, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Parole chiave
Graph convolutional network, Knowledge graph, Herbal components, Cluster analysis, Principal component analysis