图表示学习在中药组合研究中的应用探讨  

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

作　　者：何佳怡 谢佳东 胡晨骏[1] 杨涛[1] 胡孔法[1,3,4] HE Jiayi;XIE Jiadong;HU Chenjun;YANG Tao;HU Kongfa(School of Artificial Intelligence and Information Technology,Nanjing University of Chinese Medicine,Nanjing 210023,China;Institute of Information on Traditional Chinese Medicine,China Academy of Chinese Medical Sciences,Beijing 100700,China;Jiangsu Collaborative Innovation Center of Traditional Chinese Medicine in Prevention and Treatment of Tumor,Nanjing 210023,China;Jiangsu Province Engineering Research Center of TCM Intelligence Health Service,Nanjing 210023,China)

机构地区：[1]南京中医药大学人工智能与信息技术学院,江苏南京210023 [2]中国中医科学院中医药信息研究所,北京100700 [3]江苏省中医药防治肿瘤协同创新中心,江苏南京210023 [4]江苏省智慧中医药健康服务工程研究中心,江苏南京210023

出　　处：《南京中医药大学学报》2024年第12期1424-1429,共6页Journal of Nanjing University of Traditional Chinese Medicine

基　　金：国家自然科学基金面上项目(82074580);国家重点研发计划重点专项(2022YFC3502302);江苏省研究生科研创新计划(KYCX22_2054)。

摘　　要：近年来,图表示学习方法备受瞩目,它能够有效地处理图结构数据。中药以其多成分、多靶标、多通路的特点,在复杂疾病的治疗中展现出显著优势,特别是中药的不同组合能够产生独特的协同效果。图表示学习为中药组合的深入研究提供了新的视角。介绍了图表示学习的相关方法,论述了当前图表示学习方法在中药组合的应用现状,以及所面临的挑战及相应的解决方案。通过梳理该领域的研究动态和前沿趋势,旨在为后续的深入研究提供有价值的参考和启示。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 characteristics,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.

关 键 词：中药组合 图表示学习 图神经网络 

分 类 号：R2-03[医药卫生—中医学]