Year: 2025
Type: article
Abstract: Aims: Development of robust and effective methods for uncovering herb interactions and constructing herb–disease associations requires the integration of diverse biological and medical information. A key challenge in Traditional Chinese Medicine (TCM) research is to robustly uncover herb interactions and construct reliable herb–disease associations. This task requires handling the inherently high-dimensional, multi-label, and cross-domain nature of prescription data. Existing approaches provide limited representation capacity and insufficient integration of biomedical knowledge, restricting their ability to capture the complex semantics underlying the relationships between herbs and diseases. Methods: To address the limitations of existing approaches, we propose MediHerb, a multi-modal enhanced framework for disease inference via herbal knowledge. MediHerb unifies five complementary modalities: molecular sequences, fingerprints, physicochemical properties, graphical prescription representations, and the description of TCM prescriptions into a shared latent space. An attention-based fusion mechanism aligns the semantics across molecular, herbal, and diagnostic levels, enabling multi-granularity reasoning. To further promote accessibility, a lightweight graphical interface has been developed to support interaction with both the model and open datasets. Results: Experimental results on benchmark datasets demonstrate that MediHerb substantially outperforms existing baselines in herb–disease inference. Beyond predictive accuracy, the learned embeddings and model attention patterns reveal meaningful biological and pharmacological insights, confirming that MediHerb captures the mechanistic underpinnings of herb–disease associations. Conclusion: MediHerb highlights the potential of knowledge-enhanced multi-modal fusion to bridge molecular, herbal, and clinical semantics, offering a more interpretable and holistic approach to understanding TCM prescriptions.
Source: Computational Biomedicine
Authors: Xiaoyi Liu, Fu Chen, Junnan Pan, Chengwei Ai, Fei Guo, Jijun Tang
Language: English
Subfield: Computational Theory and Mathematics
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W4416623800