Research on Clustering Method for Chinese Herbal Medicine Based on Graph Neural Network
Objective This study proposes a Chinese Herbal Medicine(CHM)clustering method based on graph neural network(CHM-GCNK),aiming to discover potential compatibility of CHM at the biological network level.Methods Firstly,collect data of CHM,target,and their interactions,and construct a network of CHM and targets.Secondly,the graph neural network is used to learn the constructed network and obtain the embedded representation of CHM nodes.Then,use the Kmeans algorithm to clustering.Finally,use nonlinear dimensionality reduction technology t-SNE to visualize clustering results.Results The CHM-GCNK,Node2Vec-Kmeans,and SVD-Kmeans methods were applied to cluster 40 CHM for the treatment of lung cancer.The clustering results were five clusters,and CHM-GCNK was superior to the other two methods.The evaluation indicators SS,DBI,and CH showed results of 0.4006,0.7631,and 59.0001,respectively.Conclusion The clustering effect of CHM-GCNK is better and can be applied to the study of CHM compatibility,providing reference for the analysis methods of CHM biological networks in the era of artificial intelligence and multi omics data.
Graph neural networkProtein-Protein interaction networksMolecular biologyChinese herbal medicine clustering
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