疾病
比例(比率)
人工智能
计算机科学
集合(抽象数据类型)
冠心病
中医药
计算生物学
机器学习
传统医学
医学
替代医学
生物
内科学
物理
量子力学
程序设计语言
病理
作者
Yuanyuan Qian,Xiting Wang,Lulu Cai,Jiangxue Han,Zhu Huang,Yahui Lou,Bingyue Zhang,Yanjie Wang,Xiaoning Sun,Yan Zhang,Aisong Zhu
标识
DOI:10.1016/j.jpha.2023.12.004
摘要
Recent trends suggest that Chinese herbal medicine formulas (CHM formulas) are promising treatments for complex diseases. To characterize the precise syndromes, precise diseases and precise targets of the precise targets between complex diseases and CHM formulas, we developed an artificial intelligence-based quantitative predictive algorithm (DeepTCM). DeepTCM has gone through multilevel model calibration and validation against a comprehensive set of herb and disease data so that it accurately captures the complex cellular signaling, molecular and theoretical levels of traditional Chinese medicine (TCM). As an example, our model simulated the optimal CHM formulas for the treatment of coronary heart disease (CHD) with depression, and through model sensitivity analysis, we calculated the balanced scoring of the formulas. Furthermore, we constructed a biological knowledge graph representing interactions by associating herb-target and gene-disease interactions. Finally, we experimentally confirmed the therapeutic effect and pharmacological mechanism of a novel model-predicted intervention in humans and mice. This novel multiscale model opened up a new avenue to combine "disease syndrome" and "macro micro" system modeling to facilitate translational research in CHM formulas.
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