Application Exploration of Graph Representation Learning in Chinese Herbal Medicine Combination Research

VernacularTitle:图表示学习在中药组合研究中的应用探讨
Author: Jiayi HE ¹ ; Jiadong XIE ; Chenjun HU ; Tao YANG ; Kongfa HU
Author Information

1. 南京中医药大学人工智能与信息技术学院,江苏南京 210023;中国中医科学院中医药信息研究所,北京 100700
Publication Type:Journal Article
Keywords: Chinese herbal medicine combination; graph representation learning; graph neural networks
From: Journal of Nanjing University of Traditional Chinese Medicine 2024;40(12):1424-1429
CountryChina
Language:Chinese
Abstract: In recent years,graph representation learning methods have attracted significant attention for their ability to effectively handle graph-structured data.Chinese herbal medicine(CHM),with its multi-component,multi-target,and multi-pathway charac-teristics,demonstrates significant advantages in the treatment of complex diseases,particularly as different combinations of Chinese herbs can produce unique synergistic effects.Graph representation learning provides a new perspective for the in-depth study of CHM combinations.This paper first outlines the relevant methods of graph representation learning,explores the current application status of these methods in CHM combinations,and discusses the challenges and corresponding solutions.By reviewing the research dynamics and cutting-edge trends in this field,this paper aims to provide valuable references and insights for future in-depth research.