Yijingxiu Lu

This study introduces a context-aware hierarchical fusion model for drug relational learning, showing improved performance in predicting drug-drug interactions and side effects across different contexts.

EnsDTI improves drug-target interaction prediction by combining multiple models and providing confidence scores, achieving a 2.7% accuracy improvement over baselines.

EnsDTI-kinase is a web-server that combines eight computational models to predict kinase-inhibitor interactions, offering improved accuracy and mutagenesis analysis.