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Author

Xiucai Ye

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Oct 2026

Interpretable Protein−Nucleic Acid Binding Site Prediction via Domain-Guided Multimodal Learning

Accurate identification of protein−nucleic acid binding sites is essential for elucidating molecular mechanisms and for enabling structure-based drug discovery. Existing computational approaches struggle to jointly model sequence, structure, and domain knowledge, which limits both predictive accuracy and interpretabi...

Wen-Yu Xi, Ru-Heng Wang, Xiu-Cai Ye et al. · 0 citations
Sep 2026

Deciphering T-cell receptor-antigen recognition through interpretable residue-level interaction modeling.

Evaluation across multiple settings indicates that TCR-IFNet achieves competitive performance compared with existing methods, with higher AUPRC observed on both antigen-specific and healthy-sourced datasets, as well as improved results on independent test sets.

Wen-Yu Xi, Ru-Heng Wang, Xiu-Cai Ye et al. · 0 citations
#artificial intelligence May 2026

STE-DC2I Uncovers Driver Genes in Colorectal Cancer Subtypes Using Symbolic Trajectory-Embedded Dark Causal Inference

An explainable intelligence computational framework, Symbolic Trajectory-Embedded Dark Causal Interaction Inference (STE-DC2I), which combines symbolic trajectory embedding with historical prediction mechanisms to model nonmonotonic oscillatory dependencies between genes in CRC subtypes offers interpretable insights an...

Meng Huang, Huijin Hu, Ming Li et al. · 0 citations
Open access Jul 2026

ColdstartMHDTI: integrating biomolecular pretraining and attention-based heterogeneous graph learning for drug–target interaction prediction

ColdstartMHDTI is proposed, a two-stage framework for heterogeneous-graph-based DTI prediction that integrates sequence-derived structural representations with local and global relational information and supports candidate prioritization for downstream screening and evidence-guided hypothesis generation.

Hongyang Yang, Xiucai Ye, Huipu Han et al. · 0 citations

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