Yanyi Chu

This study uses a transformer-based generative model to generate novel protein-protein complex conformations from molecular dynamics simulations, enhancing conformational exploration.

This paper introduces a language model for 5' UTRs that improves predictions of translation efficiency and mRNA expression, and validates novel 5' UTR designs through wet-lab experiments.

The paper introduces a self-attention graph convolutional network to identify key DNA methylation sites for multi-tumor early diagnostics, achieving improved sensitivity in blood-based detection.

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.

DTI-CDF improves drug-target interaction prediction by integrating hybrid features into a cascade deep forest model, achieving higher performance than existing methods.