Shengqi Hang

scWeave: A deep learning model that bidirectionally translates between gene expression and chromatin structure at single cell resolution

scWeave translates between gene expression and chromatin structure at single-cell resolution using deep learning, improving cross-modality inference across multiple datasets.

A generalizable Hi-C foundation model for chromatin architecture, single-cell and multi-omics analysis across species

HiCFoundation is a Hi-C-based model that integrates chromatin structure with epigenomic regulation across species, improving analysis of 3D genome architecture and single-cell data.

Puget predicts gene expression across cell types using sequence and 3D chromatin organization data

Puget predicts gene expression across cell types using sequence and Hi-C data, improving generalization to new biosamples and species.

Evo2HiC: a multimodal foundation model for integrative analysis of genome sequence and architecture

Evo2HiC improves prediction of 3D genome architecture and epigenomic signals by integrating DNA sequences and Hi-C data, achieving better accuracy and computational efficiency.