A Multi-Graph Laplacian Regularized Low-Rank Representation method for cancer sample clustering with integrated TCGA data. - Juan Wang 003; Li-Hong Wang; Tian-Jing Qiao; Shasha Yuan (BIBM, 2022)

THSLRR: A Low-Rank Subspace Clustering Method Based on Tired Random Walk Similarity and Hypergraph Regularization Constraints. - Tian-Jing Qiao; Na-Na Zhang; Jin-Xing Liu 001; Junliang Shang; Cui-Na Jiao; Juan Wang 003 (SDSC, 2022)

scGASI: A Graph Autoencoder-Based Single-Cell Integration Clustering Method. - Tian-Jing Qiao; Feng Li 033; Shasha Yuan; Ling-Yun Dai; Juan Wang 003 (ISBRA, 2023)

A Personalized Low-Rank Subspace Clustering Method Based on Locality and Similarity Constraints for scRNA-seq Data Analysis. - Tian-Jing Qiao; Jin-Xing Liu 001; Junliang Shang; Shasha Yuan; Chun-Hou Zheng 001; Juan Wang 003 (IEEE J. Biomed. Health Informatics, 2023)

A Fusion Learning Model Based on Deep Learning for Single-Cell RNA Sequencing Data Clustering. - Tian-Jing Qiao; Feng Li 033; Shasha Yuan; Ling-Yun Dai; Juan Wang 003 (J. Comput. Biol., 2024)

A New Graph Autoencoder-Based Multi-Level Kernel Subspace Fusion Framework for Single-Cell Type Identification. - Juan Wang 003; Tian-Jing Qiao; Chun-Hou Zheng 001; Jin-Xing Liu 001; Junliang Shang (IEEE ACM Trans. Comput. Biol. Bioinform., 2024)

Low-Rank Multiple Kernel Model Based on Local Structures Learning and Adaptive Similarity Preserving for scRNA-seq Data Clustering. - Juan Wang 003; Tian-Jing Qiao; Zhenduo Zhang; Chun-Hou Zheng 001; Shasha Yuan (ICIC (25), 2025)