Jian Hu

MorphLink identifies disease-related morphological-molecular interplays in multi-modal spatial omics data by linking interpretable morphological features with molecular measurements.

METI integrates spatial transcriptomics and cell morphology to improve analysis of tumor microenvironments, showing consistent performance across multiple cancer types.

TESLA annotates tumor microenvironment cells and detects spatial transcriptome differences using spatial transcriptomics data.

SpaDecon improves cell-type deconvolution in spatial transcriptomics using semi-supervised learning, outperforming existing methods on real and pseudo datasets.

This paper develops an LSTM model to classify sleep stages using wrist-worn accelerometry data alone, achieving 65-81% accuracy with small training samples.

This paper introduces SpaGCN, a graph convolutional network that identifies spatial domains and spatially variable genes by integrating gene expression, spatial location, and histology in spatial transcriptomics data.

ItClust improves clustering and cell type classification in scRNA-seq by using iterative transfer learning with neural networks, outperforming existing methods across diverse datasets.

ASEP detects gene-level allele-specific expression in populations using RNA-seq, identifying differential ASE between conditions and extending findings to cardiometabolic traits.