Xiangyu Li

A Deep Learning Framework for Predicting Gut Microbe-Host Receptor Interactions

HMI-Pred predicts gut microbe-host receptor interactions using deep learning, achieving high F1-scores and validating 47% of predictions through literature.

SpaMOAL: A spatial multi-omics graph contrastive learning method for spatial domains identification

SpaMOAL identifies spatial domains by integrating spatial coordinates, histology images, and multi-omics data through graph contrastive learning, outperforming existing methods.

stMMR: accurate and robust spatial domain identification from spatially resolved transcriptomics with multi-modal feature representation

stMMR identifies spatial domains from multi-modal spatial transcriptomics data using geometric deep learning, showing improved performance in domain identification and gene discovery across biological contexts.

New gene association measures by joint network embedding of multiple gene expression datasets

The paper introduces JEBIN, a method to integrate multiple gene expression datasets into a low-dimensional gene representation, showing improved performance in co-expression network analysis and differential co-expression studies.