Yanjing Wang

ABTrans predicts anti-A{beta} antibody-peptide interactions with 0.8278 accuracy, identifying off-target candidates and cross-reactivity patterns.

SAGERank uses deep learning to predict antibody-antigen binding and docking, outperforming existing methods and showing potential for broader protein-protein interaction analysis.

This paper introduces VirEvol, a platform that predicts and visualizes SARS-CoV-2 evolutionary trends using a protein language model, structural data, and immunological insights, achieving 93% accuracy in identifying high-risk strains.

This study introduces T4SE-XGB, an XGBoost-based model for predicting type IV secreted effectors from protein sequences, achieving high accuracy and interpretability.

This study introduces DTI-MLCD, a multi-label learning method with community detection for predicting drug-target interactions, showing improved performance over existing methods.