Yu Gong Xu

PhageFlare predicts functions for phage proteins with no structural matches, outperforming baselines and annotating many previously unresolved proteins.

MetAgeFormer is a transformer-based model pre-trained on metabolomic data to estimate biological age and identify metabolic subtypes associated with disease risk.

TRAFICA improves TF-DNA binding affinity prediction by integrating open chromatin features and in vitro binding profiles, outperforming existing tools in both in vitro and in vivo settings.

stDyer improves spatial domain clustering in SRT data by combining GMVAE and GATs with dynamic graphs and mini-batch sampling, outperforming existing methods.

This paper introduces PRSIMD, a machine learning model that integrates genetic and non-genetic factors to improve disease risk prediction, showing better performance than existing PRS methods.

dynDeepDRIM uses deep learning to predict direct regulatory interactions from single-cell time-course gene expression data, outperforming existing methods.