Jialu Hu

SpatialMOC reconstructs missing spatial multi-omics data by integrating spatial context and cross-modality learning, outperforming existing methods across multiple tissues and modalities.

VISGP identifies spatially variable genes and cellular interactions from spatial transcriptomics data, outperforming existing methods in detecting ligand-receptor pairs.

The paper introduces searchSTR, a tool for genome-wide STR somatic instability analysis, and finds associations between STR instability and age, sex, brain morphology, and neurodegeneration markers.

This paper introduces stMSA, a deep learning model for integrating spatial omics data, improving domain detection, matching, and alignment across tissue slices.

DisConST improves spatial domain detection in spatial transcriptomics by using distribution-aware contrastive learning, achieving better accuracy than existing methods.

This paper introduces ALG-DDI, a multi-scale feature fusion model for drug-drug interaction prediction that outperforms existing methods by integrating attribute, local biological, and global semantic information.

DAVAE integrates single-cell data across samples, technologies, and modalities by removing batch effects and enabling transfer learning and cell type prediction.