标题： 一种可解释的人工智能框架，量化传统中医原则以增强并与现代生物医学整合 显示英文标题

标题： An Interpretable AI framework Quantifying Traditional Chinese Medicine Principles Towards Enhancing and Integrating with Modern Biomedicine

摘要： 中医的诊断和治疗原则是通过几个世纪的临床实践不断试验和修正建立起来的，它直接将特定患者的症状模式映射到个性化的草药疗法。 这些经验性的整体映射原则为解决现代生物医学中还原论方法的剩余挑战提供了有价值的策略。 然而，缺乏定量框架和分子水平的证据限制了它们的可解释性和可靠性。 在这里，我们提出了一种人工智能框架，该框架基于古代和经典的中医配方记录进行训练，以量化症状模式与草药疗法之间的映射关系。 有趣的是，我们发现经验性的中医诊断和治疗与人工智能模型中的编码-解码过程是一致的。 这使我们能够利用模型对中医原则的定量表示来构建一个可解释的中医嵌入空间（TCM-ES）。 通过广泛且广泛的中医患者数据验证，TCM-ES提供了中医实践和治疗效果的通用量化方法。 我们进一步通过对应对齐将生物医学实体映射到TCM-ES中。 我们发现TCM-ES的主要方向与关键生物功能（如代谢、免疫和稳态）显著相关，并且疾病和草药嵌入的接近性与其在人类蛋白质相互作用组中的遗传关系相一致，这表明了中医原则的生物学意义。 此外，TCM-ES揭示了潜在的疾病关系，并为现代疾病-药物对提供了替代的评估指标。 最后，我们构建了一个全面且综合的中医知识图谱，该图谱预测疾病与靶点、药物、草药化合物和草药疗法之间的潜在关联，为疾病分析和药物开发提供了基于中医的机遇。 显示英文摘要

摘要： Traditional Chinese Medicine diagnosis and treatment principles, established through centuries of trial-and-error clinical practice, directly maps patient-specific symptom patterns to personalised herbal therapies. These empirical holistic mapping principles offer valuable strategies to address remaining challenges of reductionism methodologies in modern biomedicine. However, the lack of a quantitative framework and molecular-level evidence has limited their interpretability and reliability. Here, we present an AI framework trained on ancient and classical TCM formula records to quantify the symptom pattern-herbal therapy mappings. Interestingly, we find that empirical TCM diagnosis and treatment are consistent with the encoding-decoding processes in the AI model. This enables us to construct an interpretable TCM embedding space (TCM-ES) using the model's quantitative representation of TCM principles. Validated through broad and extensive TCM patient data, the TCM-ES offers universal quantification of the TCM practice and therapeutic efficacy. We further map biomedical entities into the TCM-ES through correspondence alignment. We find that the principal directions of the TCM-ES are significantly associated with key biological functions (such as metabolism, immune, and homeostasis), and that the disease and herb embedding proximity aligns with their genetic relationships in the human protein interactome, which demonstrate the biological significance of TCM principles. Moreover, the TCM-ES uncovers latent disease relationships, and provides alternative metric to assess clinical efficacy for modern disease-drug pairs. Finally, we construct a comprehensive and integrative TCM knowledge graph, which predicts potential associations between diseases and targets, drugs, herbal compounds, and herbal therapies, providing TCM-informed opportunities for disease analysis and drug development.