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Identifying Key Node with Motif-Based PageRank on Acupoint-Disease Network.

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This study introduces a new algorithm for identifying key acupoints in acupuncture networks by considering higher-order interactions. The novel method improves accuracy and offers a more reliable approach to optimizing acupuncture prescriptions.

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Area of Science:

  • Network Science
  • Traditional Chinese Medicine
  • Computational Biology

Background:

  • Existing key acupoint mining algorithms use binary synergy, neglecting higher-order interactions and meridian patterns.
  • Current assessment methods lack broad applicability and universality.

Purpose of the Study:

  • To propose a high-specificity key acupoint mining algorithm using 3-node motifs in acupoint-disease networks (ADN).
  • To introduce universal evaluation criteria (resolution, network loss, accuracy) for algorithm assessment.
  • To identify key acupoints with significant global network impact based on traditional Chinese medicine principles.

Main Methods:

  • Developed a key acupoint mining algorithm based on 3-node motifs within the acupoint-disease network (ADN).
  • Introduced new, universal evaluation criteria: resolution, network loss, and accuracy.
  • Divided acupoints into 19 regions based on distribution characteristics for analysis.

Main Results:

  • The proposed algorithm achieved 63% accuracy in identifying key acupoints, outperforming existing methods by 14-21%.
  • Identified key acupoints significantly disrupt network connectivity and exhibit high synergistic cooperation.
  • Demonstrated stability and specificity, ensuring the reliability of identified key acupoints.

Conclusions:

  • The algorithm reliably identifies core acupoints based on network topology and high synergy.
  • Findings support optimizing acupuncture prescriptions by exploring targeted, high-impact acupoint combinations.
  • This approach enhances understanding of acupoint relationships within meridian systems.