A Semantic Analysis and Community Detection-Based Artificial Intelligence Model for Core Herb Discovery from the Literature: Taking Chronic Glomerulonephritis Treatment as a Case Study

<div>The process of LILPA. LILPA first initializes each node with a unique label, chooses nodes to update according to node importance, and moves nodes in the layout area according to node attraction. Then, the neighbour nodes of the updating node launch labels, and the updating node accepts labels according to label importance. The above steps except initialization are iteratively executed until all nodes are updated once. If LILPA reaches termination condition, then it goes to postprocessing, else, it returns to the step of node choice for the next iteration.</div>

Computational and Mathematical Methods in Medicine

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Figure 5

Figure 5: A Semantic Analysis and Community Detection-Based Artificial Intelligence Model for Core Herb Discovery from the Literature: Taking Chronic Glomerulonephritis Treatment as a Case Study 

Figure 5 | A Semantic Analysis and Community Detection-Based Artificial Intelligence Model for Core Herb Discovery from the Literature: Taking Chronic Glomerulonephritis Treatment as a Case Study 