Ruiqing Zheng

scHiR learns joint representations from paired scRNA-seq and scHi-C data, improving clustering and trajectory inference while recovering input signals.

DPStab predicts protein stability changes from single mutations using self-distillation and antisymmetric constraints, achieving state-of-the-art performance in ΔΔG and ΔTm prediction.

OmegaTherm predicts mutation-induced protein stability responses by jointly modeling folding free energy and melting temperature, achieving state-of-the-art performance in prediction and variant ranking.

SpaRank is a transferable spatial deconvolution framework that uses rank-based encodings to robustly infer cell-type compositions across diverse biological contexts and omics modalities.

SpaMosaic integrates spatial multi-omics data using contrastive learning and graph neural networks, improving spatial domain identification and missing modality imputation across diverse datasets.

GRAVITY improves RNA velocity modeling by integrating gene regulatory networks, enabling accurate trajectory inference and discovery of terminal cell states in embryonic brain development.

This paper introduces a hybrid network-based method (HyMSMK) to identify disease-related genes by incorporating multi-scale module kernels in biomolecule networks, showing improved performance over existing methods.

ACE provides a contrastive learning framework for mosaic integration of single-cell multi-omics data, improving representation of cellular heterogeneities in datasets with incomplete modalities.

SSRE improves cell type identification in scRNA-seq by enhancing similarity measurement through sparse subspace representation and gene selection.