Han Shu

ctDNA-BERT scores individual bisulfite reads for cell-free DNA methylation analysis, achieving high accuracy in cancer staging without cohort-specific retraining.

This study benchmarks preprocessing workflows for spatial transcriptomics data across multiple platforms and identifies platform-specific recommendations to improve downstream analysis.

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

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.