Herb–disease association prediction is central to computational traditional medicine, but existing graph and hypergraph methods mainly rely on observed topology and underuse biomedical textual semantics, especially in heterogeneous or sparse association networks.
We propose LLM-H2G, a biomedical semantic...
Jun Zhang, Hengchuang Yin, Chao Wu et al.· BMC Bioinformatics· 0 citations
Large language models (LLMs) have significantly revolutionized natural language processing through their strong capabilities in text generation and reasoning. Yet, their applicability to bioinformatics applications remains largely unexplored. Here, we systematically evaluate state‐of‐the‐art LLMs across six represent...
Hengchuang Yin, Zi-Wen Cui, Dong-Xu Li et al.· Advanced Computing· 0 citations
RGIGC formulates receptive field configuration as a structural reasoning problem and employs reinforcement learning with two Q-learning agents to infer and optimize the structural receptive field of each node to enhance the interpretability of the resulting clusters.
Yue Yang, Dongxu Li, Hengchuang Yin et al.· Proceedings of the 32nd ACM...· 0 citations
The DRHIN platform provides a code-free portal supporting three key predictive tasks: discovering drug-disease associations, repurposing existing drugs for new indications, and identifying potential therapies for specific diseases, making analyses accessible and reproducible.
Bowei Zhao, Dongxu Li, Yue Yang et al.· Journal of Chemical Informat...· 10 citations· ⚡1
Graph clustering aims to group nodes into meaningful clusters, a crucial task for understanding the behavior of complex networks and their underlying structural organization. Leveraging the expressive power of deep learning, graph neural networks (GNNs) have emerged as popular tools for performing graph clustering with...
Yue Yang, Dongxu Li, Hengchuang Yin et al.· Proceedings of the 32nd ACM...· 0 citations
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