Identification of Key Nodes of Acupoint-Disease Network Based on Motif PageRank Algorithm

doi:10.12068/j.issn.1005-3026.2024.05.003

Abstract

Abstract:

Aiming at the problems of poor accuracy and narrow applicability of the existing key acupoint mining algorithms， a high specificity acupoint mining algorithm based on a 3‐node motif is proposed by introducing higher‐order interactions between multiple acupoints in the acupoint‐disease network. Comparing this algorithm with five other acupoint importance assessment algorithms in terms of resolution， network loss， and accuracy， the results show that the key acupoints identified by this algorithm have obvious destructive effects on the connectivity of the network， which indicates that the key acupoints are the core of the topology of acupoint‐disease network and have high synergistic co?operation with other acupoints. The stability of this algorithm ensures the reliability of the key acupoints. From the perspective of the network topology and the high synergy between acupoints， the key acupoints found by the algorithm can be used as the core acupoints in the acupoint network， helping researchers to explore targeted and highly effective combinations of acupoints.

Key words: acupoint, disease, motif, network, PageRank algorithm

CLC Number:

TP 301.6

Hai ZHAO, Jiu-nan MIAO, Xiao LIU, Xue-long YU. Identification of Key Nodes of Acupoint-Disease Network Based on Motif PageRank Algorithm[J]. Journal of Northeastern University(Natural Science), 2024, 45(5): 628-635.

Figures/Tables 9

Table 1 Network attributes

网络	节点数量	边缘数	平均加权度	平均聚类系数
$G$	197	2 373	40.660	0.763

Table 1 Network attributes

网络	节点数量	边缘数	平均加权度	平均聚类系数
$G$	197	2 373	40.660	0.763

Fig.1 Degree distribution in ADN

Fig.2 Example of 3‐node simple motif

Fig.3 Synergistic strength distribution

Fig.4 Example of weight matrix

Fig.5 Complementary cumulative distribution function for the ranking distributions of ranking list offered by six algorithms

Table 2 Network efficiency comparisons of six algorithms

算法	MPR	Key Node	CLD	CC	BC	EC
效率	0.000 8	2.472 1	2.230 7	2.630 6	2.424 9	2.487 5

Fig.6 Toy examples of WC model

Table 3 Accuracy comparisons of six algorithms

算法	MPR	Key Node	CLD	CC	BC	EC
准确度/%	63.360	49.191	42.100	50.939	52.659	43.100

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