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Author

Ying Chang

University of Chinese Academy of Sciences

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6 papers indexed here

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#protein folding Oct 2026

StructAMP: A Residual Multi-Modal Framework for Leakage-Controlled Antimicrobial Peptide Prediction.

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. · 0 citations
Open access Sep 2026

Evaluating Large‐Language Models in Bioinformatics Applications

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. · 0 citations
Book Open access Aug 2026

Reinforced Structural Reasoning for Receptive Field Optimization in GNN toward Interpretable Graph Clustering

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. · 0 citations
#natural language process... Preprint Aug 2026

Self-Evolving Embodied Agents via Skill-Harness Evolution

This work proposes SHAPER, a self-evolving framework for train-free embodied adaptation that keeps model parameters frozen and improves the non-parametric agent system by evolving reusable skills and a context-code harness through target-environment rollouts.

Pei-Dong Wang, Zhiming Ma, Ying Chang et al. · 7 citations · ⚡1
Book Open access Aug 2026

Reinforced Structural Reasoning for Receptive Field Optimization in GNN toward Interpretable Graph Clustering

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. · 0 citations

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