Kui Xu

Research Scientist

[1] CryoNet.Refine: A One-step Diffusion Model for Rapid Refinement of Structural Models with Cryo-EM Density Map Restraints (ICLR, 2026);

[2] CryoLVM: Self-supervised Learning from Cryo-EM Density Maps with Large Vision Models (ICLR, 2026);

[3] CryoDomain: Sequence-free Protein Domain Identification from Low-resolution Cryo-EM Density Maps (AAAI, 2025);

[4] PrismNet: predicting protein–RNA interaction using in vivo RNA structural information (NAR, 2023);

[5] Structural basis of membrane skeleton organization in red blood cells (Cell, 2023);

[6] CryoRes: Local Resolution Estimation of Cryo-EM Density Maps by Deep Learning (JMB, 2023);

[7] A deep learning method for recovering missing signals in transcriptome-wide RNA structure profiles from probing experiments (Nature MI, 2021);

[8] In vivo structural characterization of the SARS-CoV-2 RNA genome identifies host proteins vulnerable to repurposed drugs (Cell, 2021);

[9] Predicting dynamic cellular protein–RNA interactions by deep learning using in vivo RNA structures (Cell Research, 2021);

[10] Structure of the activated human minor spliceosome (Science, 2021);

[11] RASP: an atlas of transcriptome-wide RNA secondary structure probing data (NAR, 2020);

[12] VRmol: an Integrative Web-Based Virtual Reality System to Explore Macromolecular Structure (Bioinformatics, 2020);

[13] SCALE method for single-cell ATAC-seq analysis via latent feature extraction (Nature Comms, 2019);

[14] A²-Net: Molecular Structure Estimation from Cryo-EM Density Volumes (AAAI, 2019);

[15] RISE: a database of RNA interactome from sequencing experiments (NAR, 2018);

Ruiyun Yang

Research Assistant

Kang Tian Ph.D

[1] Joint analysis of chromatin accessibility and gene expression in the same single cells reveals cancer-specific regulatory programs (Cell Systems, 2025);

[2] Online single-cell data integration through projecting heterogeneous datasets into a common cell-embedding space (Nature Comms, 2022);

[3] SCALE method for single-cell ATAC-seq analysis via latent feature extraction (Nature Comms, 2019);

Yuzhe Li Ph.D

[1] Joint analysis of chromatin accessibility and gene expression in the same single cells reveals cancer-specific regulatory programs (Cell Systems, 2025);

[2] Tissue module discovery in single-cell-resolution spatial transcriptomics data via cell-cell interaction-aware cell embedding (Cell Systems, 2024);

[3] Online single-cell data integration through projecting heterogeneous datasets into a common cell-embedding space (Nature Comms, 2022);

[4] The chromatin-remodeling enzyme Smarca5 regulates erythrocyte aggregation via Keap1-Nrf2 signaling (eLife, 2021);

Pengfei Wang Ph.D

[1] Predicting small molecule–RNA interactions without RNA tertiary structures (Nature Biotech, 2026);

[2] Computational prediction and experimental validation identify functionally conserved lncRNAs from zebrafish to human (Nature Genetics, 2024);

Jingle Xu

PhD student

[1] VRmol: an Integrative Web-Based Virtual Reality System to Explore Macromolecular Structure;

Jianghui Zhu

PhD student

[1] PrismNet: predicting protein–RNA interaction using in vivo RNA structural information;

[2] Recent advances in RNA structurome;

[3] An ultra low-input method for global RNA structure probing uncovers Regnase-1-mediated regulation in macrophages;

Muzhi Dai

PhD student

[1] CryoDomain: Sequence-free Protein Domain Identification from Low-resolution Cryo-EM Density Maps (AAAI, 2025);

[2] CryoRes: Local Resolution Estimation of Cryo-EM Density Maps by Deep Learning (JMB, 2023);

Jiasheng Zhang

PhD student

[1] Predicting small molecule–RNA interactions without RNA tertiary structures (Nature Biotech, 2026);

Weixi Ning

PhD student

[1] Online single-cell data integration through projecting heterogeneous datasets into a common cell-embedding space;

Jianbo Ma

PhD student

[1] Predicting small molecule–RNA interactions without RNA tertiary structures (Nature Biotech, 2026);

Zihan Dominic Li

PhD student

RNA-targeting drug discovery

Kunting Mu

PhD student

[1] RASP v2.0:an updated atlas for RNA structure probing data (Nucleic Acids Research, 2024);

Zilin Cai

PhD student

[1] Predicting small molecule–RNA interactions without RNA tertiary structures (Nature Biotech, 2026);

Xinyue Shan

PhD student

[1] Predicting small molecule–RNA interactions without RNA tertiary structures (Nature Biotech, 2026);

Weining Fu

PhD student

[1] CryoLVM: Self-supervised Learning from Cryo-EM Density Maps with Large Vision Models (ICLR, 2026);

[2] CryoDomain: Sequence-free Protein Domain Identification from Low-resolution Cryo-EM Density Maps (AAAI, 2025);

Xiaolei Fu

PhD student

Unbiased capture of physical cell-cell interaction networks in complex tissues at scale, High-throughput screening technology for capturing antibody-antigen interactions

Yingjia Xu

PhD student

single cell technology, cell-cell interaction

Xin Wen

PhD student

Deep learning, RNA

Jianhua Huang

PhD student

RNA structure, Cryo-EM

Kaile Huang

PhD student

RNA Biology

Yumeng Chen

PhD student

Deep learning, Single-cell omics

Songyang Li

PhD student

Intelligent Optimization Algorithm, Biomacromolecular Structure

Xudong Zeng

PhD student

Spatial omics, Virtual tissue