Reliable evaluation of antimicrobial peptide (AMP) predictors is complicated by train-test sequence-similarity leakage and mismatched comparisons with strong protein language model baselines. We present a leakage-controlled framework that quantifies auxiliary-modality value through zero-initialized residual corrections...
Ming-Jian Zhang, Guo-Dong Li, Ying Chang et al.· IEEE transactions on computa...· 0 citations
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
Single-cell RNA sequencing profiles cellular heterogeneity at atlas scale, making automated annotation essential. However, target datasets often contain novel cell types missing from incomplete references. We present scOLAR, an ontology-guided open-set framework that learns prototypes over the Cell Ontology and uses bo...
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
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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