A Two-Stage Sparse Selection Method for Extracting Characteristic Genes. - Ying-Lian Gao; Jin-Xing Liu 001; Chun-Hou Zheng 001; Shengjun Li; Yuxia Lei (ICIC (2), 2015)

Semi-supervised Feature Extraction for RNA-Seq Data Analysis. - Jin-Xing Liu 001; Yong Xu 001; Ying-Lian Gao; Dong Wang 019; Chun-Hou Zheng 001; Junliang Shang (ICIC (3), 2015)

Graph Regularized Non-negative Matrix with L0-Constraints for Selecting Characteristic Genes. - Chun-Xia Ma; Ying-Lian Gao; Dong Wang 019; Jian Liu; Jin-Xing Liu 001 (ICIC (2), 2015)

Application of Graph Regularized Non-negative Matrix Factorization in Characteristic Gene Selection. - Dong Wang 019; Ying-Lian Gao; Jin-Xing Liu 001; Jiguo Yu; Chang-Gang Wen (ICIC (2), 2015)

A graph-Laplacian PCA based on L1/2-norm constraint for characteristic gene selection. - Chun-Mei Feng 001; Jin-Xing Liu 001; Ying-Lian Gao; Juan Wang 003; Dong-Qin Wang; Yong Du (BIBM, 2016)

Characteristic gene selection via L2, 1-norm Sparse Principal Component Analysis. - Yao Lu 008; Ying-Lian Gao; Jin-Xing Liu 001; Chang-Gang Wen; Yaxuan Wang; Jiguo Yu (BIBM, 2016)

Differentially expressed genes selection via Truncated Nuclear Norm Regularization. - Yaxuan Wang; Jin-Xing Liu 001; Ying-Lian Gao; Xiangzhen Kong; Chun-Hou Zheng 001; Yong Du (BIBM, 2016)

L21-iPaD: An efficient method for drug-pathway association pairs inference. - Dong-Qin Wang; Chun-Hou Zheng 001; Ying-Lian Gao; Jin-Xing Liu 001; Sha-Sha Wu; Junliang Shang (BIBM, 2016)

A Simple Review of Sparse Principal Components Analysis. - Chun-Mei Feng 001; Ying-Lian Gao; Jin-Xing Liu 001; Chun-Hou Zheng 001; Shengjun Li; Dong Wang 019 (ICIC (2), 2016)

Comparison of Non-negative Matrix Factorization Methods for Clustering Genomic Data. - Mi-Xiao Hou; Ying-Lian Gao; Jin-Xing Liu 001; Junliang Shang; Chun-Hou Zheng 001 (ICIC (2), 2016)

Differentially expressed genes selection via Laplacian regularized low-rank representation method. - Yaxuan Wang; Jin-Xing Liu 001; Ying-Lian Gao; Chun-Hou Zheng 001; Junliang Shang (Comput. Biol. Chem., 2016)

A Class-Information-Based Sparse Component Analysis Method to Identify Differentially Expressed Genes on RNA-Seq Data. - Jin-Xing Liu 001; Yong Xu 001; Ying-Lian Gao; Chun-Hou Zheng 001; Dong Wang 019; Qi Zhu 001 (IEEE ACM Trans. Comput. Biol. Bioinform., 2016)

Characteristic Gene Selection Based on Robust Graph Regularized Non-Negative Matrix Factorization. - Dong Wang 019; Jin-Xing Liu 001; Ying-Lian Gao; Chun-Hou Zheng 001; Yong Xu 001 (IEEE ACM Trans. Comput. Biol. Bioinform., 2016)

Low-rank representation regularized by L2, 1-norm for identifying differentially expressed genes. - Yaxuan Wang; Jin-Xing Liu 001; Ying-Lian Gao; Chun-Hou Zheng 001; Ling-Yun Dai (BIBM, 2017)

Feature selection and clustering via robust graph-laplacian PCA based on capped L1-norm. - Ming-Juan Wu; Jin-Xing Liu 001; Ying-Lian Gao; Xiangzhen Kong; Chun-Mei Feng 001 (BIBM, 2017)

Graph regularized robust non-negative matrix factorization for clustering and selecting differentially expressed genes. - Na Yu; Jin-Xing Liu 001; Ying-Lian Gao; Chun-Hou Zheng 001; Juan Wang 003; Ming-Juan Wu (BIBM, 2017)

Identifying drug-pathway association pairs based on L2, 1-integrative penalized matrix decomposition. - Jin-Xing Liu 001; Dong-Qin Wang; Chun-Hou Zheng 001; Ying-Lian Gao; Sha-Sha Wu; Junliang Shang (BMC Syst. Biol., 2017)

A novel low-rank representation method for identifying differentially expressed genes. - Xiu-Xiu Xu; Ying-Lian Gao; Jin-Xing Liu 001; Yaxuan Wang; Ling-Yun Dai; Xiang-Zhen Kong; Shasha Yuan (Int. J. Data Min. Bioinform., 2017)

A joint-L2, 1-norm-constraint-based semi-supervised feature extraction for RNA-Seq data analysis. - Jin-Xing Liu 001; Dong Wang 019; Ying-Lian Gao; Chun-Hou Zheng 001; Junliang Shang; Feng Liu 013; Yong Xu 001 (Neurocomputing, 2017)

Hypergraph regularized NMF by L2, 1-norm for Clustering and Com-abnormal Expression Genes Selection. - Na Yu; Ying-Lian Gao; Jin-Xing Liu 001; Juan Wang 003; Junliang Shang (BIBM, 2018)

Performance Analysis of Non-negative Matrix Factorization Methods on TCGA Data. - Mi-Xiao Hou; Jin-Xing Liu 001; Junliang Shang; Ying-Lian Gao; Xiang-Zhen Kong; Ling-Yun Dai (ICIC (2), 2018)

Regularized Non-Negative Matrix Factorization for Identifying Differentially Expressed Genes and Clustering Samples: A Survey. - Jin-Xing Liu 001; Dong Wang 019; Ying-Lian Gao; Chun-Hou Zheng 001; Yong Xu 001; Jiguo Yu (IEEE ACM Trans. Comput. Biol. Bioinform., 2018)

Dual Sparse Collaborative Matrix Factorization Method Based on Gaussian Kernel Function for Predicting LncRNA-Disease Associations. - Ming-Ming Gao; Zhen Cui; Ying-Lian Gao; Feng Li 033; Jin-Xing Liu 001 (ICIC (3), 2019)

DSNPCMF: Predicting MiRNA-Disease Associations with Collaborative Matrix Factorization Based on Double Sparse and Nearest Profile. - Meng-Meng Yin; Zhen Cui; Jin-Xing Liu 001; Ying-Lian Gao; Xiang-Zhen Kong (IDMB, 2019)

An Integrated Graph Regularized Non-Negative Matrix Factorization Model for Gene Co-Expression Network Analysis. - Ying-Lian Gao; Mi-Xiao Hou; Jin-Xing Liu 001; Xiang-Zhen Kong (IEEE Access, 2019)

L2, 1-GRMF: an improved graph regularized matrix factorization method to predict drug-target interactions. - Zhen Cui; Ying-Lian Gao; Jin-Xing Liu 001; Ling-Yun Dai; Shasha Yuan (BMC Bioinform., 2019)

RCMF: a robust collaborative matrix factorization method to predict miRNA-disease associations. - Zhen Cui; Jin-Xing Liu 001; Ying-Lian Gao; Chun-Hou Zheng 001; Juan Wang 003 (BMC Bioinform., 2019)

NPCMF: Nearest Profile-based Collaborative Matrix Factorization method for predicting miRNA-disease associations. - Ying-Lian Gao; Zhen Cui; Jin-Xing Liu 001; Juan Wang 003; Chun-Hou Zheng 001 (BMC Bioinform., 2019)

Network analysis based on low-rank method for mining information on integrated data of multi-cancers. - Mi-Xiao Hou; Ying-Lian Gao; Jin-Xing Liu 001; Ling-Yun Dai; Xiang-Zhen Kong; Junliang Shang (Comput. Biol. Chem., 2019)

Hypergraph Regularized Discriminative Nonnegative Matrix Factorization on Sample Classification and Co-Differentially Expressed Gene Selection. - Yong-Jing Hao; Ying-Lian Gao; Mi-Xiao Hou; Ling-Yun Dai; Jin-Xing Liu 001 (Complex., 2019)

Differentially Expressed Genes Extracted by the Tensor Robust Principal Component Analysis (TRPCA) Method. - Yue Hu; Jin-Xing Liu 001; Ying-Lian Gao; Shengjun Li; Juan Wang 003 (Complex., 2019)

Supervised Discriminative Sparse PCA for Com-Characteristic Gene Selection and Tumor Classification on Multiview Biological Data. - Chun-Mei Feng 001; Yong Xu 001; Jin-Xing Liu 001; Ying-Lian Gao; Chun-Hou Zheng 001 (IEEE Trans. Neural Networks Learn. Syst., 2019)

The computational prediction of drug-disease interactions using the dual-network L2,1-CMF method. - Zhen Cui; Ying-Lian Gao; Jin-Xing Liu 001; Juan Wang 003; Junliang Shang; Ling-Yun Dai (BMC Bioinform., 2019)

Locally Manifold Non-negative Matrix Factorization Based on Centroid for scRNA-seq Data Analysis. - Chuan-Yuan Wang; Ying-Lian Gao; Cui-Na Jiao; Jin-Xing Liu 001; Chunhou Zheng 001; Xiang-Zhen Kong (BIBM, 2020)

Robust Graph Regularized Extreme Learning Machine Auto Encoder and Its Application to Single-Cell Samples Classification. - Liang-Rui Ren; Jin-Xing Liu 001; Ying-Lian Gao; Xiang-Zhen Kong; Chun-Hou Zheng 001 (ICIC (2), 2020)

Correntropy induced loss based sparse robust graph regularized extreme learning machine for cancer classification. - Liang-Rui Ren; Ying-Lian Gao; Jin-Xing Liu 001; Junliang Shang; Chun-Hou Zheng 001 (BMC Bioinform., 2020)

MCCMF: collaborative matrix factorization based on matrix completion for predicting miRNA-disease associations. - Tian-Ru Wu; Meng-Meng Yin; Cui-Na Jiao; Ying-Lian Gao; Xiang-Zhen Kong; Jin-Xing Liu 001 (BMC Bioinform., 2020)

L2, 1-Extreme Learning Machine: An Efficient Robust Classifier for Tumor Classification. - Liang-Rui Ren; Ying-Lian Gao; Jin-Xing Liu 001; Rong Zhu; Xiang-Zhen Kong (Comput. Biol. Chem., 2020)

A multi-view classification and feature selection method via sparse low-rank regression analysis. - Yao Lu 008; Ying-Lian Gao; Pei-Yong Li; Jin-Xing Liu 001 (Int. J. Data Min. Bioinform., 2020)

LncRNA-Disease Associations Prediction Using Bipartite Local Model With Nearest Profile-Based Association Inferring. - Zhen Cui; Jin-Xing Liu 001; Ying-Lian Gao; Rong Zhu; Shasha Yuan (IEEE J. Biomed. Health Informatics, 2020)

Hyper-Graph Regularized Constrained NMF for Selecting Differentially Expressed Genes and Tumor Classification. - Cui-Na Jiao; Ying-Lian Gao; Na Yu; Jin-Xing Liu 001; Lianyong Qi (IEEE J. Biomed. Health Informatics, 2020)

Integrative Hypergraph Regularization Principal Component Analysis for Sample Clustering and Co-Expression Genes Network Analysis on Multi-Omics Data. - Ming-Juan Wu; Ying-Lian Gao; Jin-Xing Liu 001; Chun-Hou Zheng 001; Juan Wang 003 (IEEE J. Biomed. Health Informatics, 2020)

Sparse Hyper-graph Non-negative Matrix Factorization by Maximizing Correntropy. - Cui-Na Jiao; Jin-Xing Liu 001; Ying-Lian Gao; Xiang-Zhen Kong; Chun-Hou Zheng 001; Xianzi Yu (BIBM, 2021)

Robust Tensor Method Based on Correntropy and Tensor Singular Value Decomposition for Cancer Genomics Data. - Qian Qiao; Ying-Lian Gao; Shasha Yuan; Jin-Xing Liu 001 (BIBM, 2021)

Adaptive total-variation joint learning model for analyzing single cell RNA seq data. - Dai-Jun Zhang; Jing-Xiu Zhao; Jin-Xing Liu 001; Ying-Lian Gao (BIBM, 2021)

Extreme Learning Machine Based on Double Kernel Risk-Sensitive Loss for Cancer Samples Classification. - Zhen-Xin Niu; Liang-Rui Ren; Rong Zhu; Xiang-Zhen Kong; Ying-Lian Gao; Jin-Xing Liu 001 (ICIC (2), 2021)

MKL-LP: Predicting Disease-Associated Microbes with Multiple-Similarity Kernel Learning-Based Label Propagation. - Ying-Lian Gao; Meng-Meng Yin; Jin-Xing Liu 001; Junliang Shang; Chun-Hou Zheng 001 (ISBRA, 2021)

DSCMF: prediction of LncRNA-disease associations based on dual sparse collaborative matrix factorization. - Jin-Xing Liu 001; Ming-Ming Gao; Zhen Cui; Ying-Lian Gao; Feng Li 033 (BMC Bioinform., 2021)

Sparse robust graph-regularized non-negative matrix factorization based on correntropy. - Chuan-Yuan Wang; Ying-Lian Gao; Jin-Xing Liu 001; Ling-Yun Dai; Junliang Shang (J. Bioinform. Comput. Biol., 2021)

Kernel Risk-Sensitive Loss based Hyper-graph Regularized Robust Extreme Learning Machine and Its Semi-supervised Extension for Classification. - Liang-Rui Ren; Jin-Xing Liu 001; Ying-Lian Gao; Xiang-Zhen Kong; Chun-Hou Zheng 001 (Knowl. Based Syst., 2021)

DSTPCA: Double-Sparse Constrained Tensor Principal Component Analysis Method for Feature Selection. - Yue Hu; Jin-Xing Liu 001; Ying-Lian Gao; Junliang Shang (IEEE ACM Trans. Comput. Biol. Bioinform., 2021)

Dual Hyper-Graph Regularized Supervised NMF for Selecting Differentially Expressed Genes and Tumor Classification. - Chuan-Yuan Wang; Na Yu; Ming-Juan Wu; Ying-Lian Gao; Jin-Xing Liu 001; Juan Wang 003 (IEEE ACM Trans. Comput. Biol. Bioinform., 2021)

LWPCMF: Logistic Weighted Profile-Based Collaborative Matrix Factorization for Predicting MiRNA-Disease Associations. - Meng-Meng Yin; Zhen Cui; Ming-Ming Gao; Jin-Xing Liu 001; Ying-Lian Gao (IEEE ACM Trans. Comput. Biol. Bioinform., 2021)

Multi-Label Fusion Collaborative Matrix Factorization for Predicting LncRNA-Disease Associations. - Ming-Ming Gao; Zhen Cui; Ying-Lian Gao; Juan Wang 003; Jin-Xing Liu 001 (IEEE J. Biomed. Health Informatics, 2021)

WGRCMF: A Weighted Graph Regularized Collaborative Matrix Factorization Method for Predicting Novel LncRNA-Disease Associations. - Jin-Xing Liu 001; Zhen Cui; Ying-Lian Gao; Xiang-Zhen Kong (IEEE J. Biomed. Health Informatics, 2021)

An integrated Extreme learning machine based on kernel risk-sensitive loss of q-Gaussian and voting mechanism for sample classification. - Zhi-Yuan Li; Ying-Lian Gao; Zhen-Xin Niu; Shasha Yuan; Chun-Hou Zheng 001; Jin-Xing Liu 001 (BIBM, 2022)

HSAELDA: Predicting lncRNA-disease associations based on heterogeneous networks and Stacked Autoencoder. - Wen-Yu Xi; Qianqian Ren; Jin-Xing Liu 001; Ying-Lian Gao (BIBM, 2022)

Predicting LncRNA-Disease Associations Based on LncRNA-MiRNA-Disease Multilayer Association Network and Bipartite Network Recommendation. - Guozheng Zhang; Shu-Zhen Li; Xu-Ran Dou; Junliang Shang; Qianqian Ren; Ying-Lian Gao (BIBM, 2022)

A Locality-Constrained Linear Coding-Based Ensemble Learning Framework for Predicting Potentially Disease-Associated MiRNAs. - Yi Shen; Ying-Lian Gao; Shu-Zhen Li; Boxin Guan; Jin-Xing Liu 001 (ISBRA, 2022)

MLMVFE: A Machine Learning Approach Based on Muli-view Features Extraction for Drug-Disease Associations Prediction. - Ying Wang 143; Ying-Lian Gao; Juan Wang 003; Junliang Shang; Jin-Xing Liu 001 (ISBRA, 2022)

A new framework for drug-disease association prediction combing light-gated message passing neural network and gated fusion mechanism. - Bao-Min Liu; Ying-Lian Gao; Dai-Jun Zhang; Feng Zhou 021; Juan Wang 003; Chun-Hou Zheng 001; Jin-Xing Liu 001 (Briefings Bioinform., 2022)

Multi-similarity fusion-based label propagation for predicting microbes potentially associated with diseases. - Meng-Meng Yin; Ying-Lian Gao; Junliang Shang; Chun-Hou Zheng 001; Jin-Xing Liu 001 (Future Gener. Comput. Syst., 2022)

Tensor decomposition based on the potential low-rank and p-shrinkage generalized threshold algorithm for analyzing cancer multiomics data. - Hang-Jin Yang; Yuxia Lei; Juan Wang 003; Xiang-Zhen Kong; Jin-Xing Liu 001; Ying-Lian Gao (J. Bioinform. Comput. Biol., 2022)

Kernel risk-sensitive mean p-power error based robust extreme learning machine for classification. - Liang-Rui Ren; Ying-Lian Gao; Junliang Shang; Jin-Xing Liu 001 (Int. J. Mach. Learn. Cybern., 2022)

Robust Principal Component Analysis Based On Hypergraph Regularization for Sample Clustering and Co-Characteristic Gene Selection. - Ying-Lian Gao; Ming-Juan Wu; Jin-Xing Liu 001; Chun-Hou Zheng 001; Juan Wang 003 (IEEE ACM Trans. Comput. Biol. Bioinform., 2022)

Single-Cell RNA Sequencing Data Clustering by Low-Rank Subspace Ensemble Framework. - Chuan-Yuan Wang; Ying-Lian Gao; Jin-Xing Liu 001; Xiang-Zhen Kong; Chun-Hou Zheng 001 (IEEE ACM Trans. Comput. Biol. Bioinform., 2022)

NCPLP: A Novel Approach for Predicting Microbe-Associated Diseases With Network Consistency Projection and Label Propagation. - Meng-Meng Yin; Jin-Xing Liu 001; Ying-Lian Gao; Xiang-Zhen Kong; Chun-Hou Zheng 001 (IEEE Trans. Cybern., 2022)

Unsupervised Cluster Analysis and Gene Marker Extraction of scRNA-seq Data Based On Non-Negative Matrix Factorization. - Chuan-Yuan Wang; Ying-Lian Gao; Xiang-Zhen Kong; Jin-Xing Liu 001; Chun-Hou Zheng 001 (IEEE J. Biomed. Health Informatics, 2022)

GRPGAT: Predicting CircRNA-disease Associations Based on Graph Random Propagation Network and Graph Attention Network. - Wen-Yue Kang; Chun-Hou Zheng 001; Ying-Lian Gao; Juan Wang 003; Junliang Shang; Jin-Xing Liu 001 (BIBM, 2023)

MKGSAGE: A Computational Framework via Multiple Kernel Fusion on GraphSAGE for Inferring Potential Disease-Related Microbes. - Shuang Wang; Jin-Xing Liu 001; Bao-Min Liu; Ling-Yun Dai; Feng Li 033; Ying-Lian Gao (BIBM, 2023)

LANCMDA: Predicting MiRNA-Disease Associations via LightGBM with Attributed Network Construction. - Xu-Ran Dou; Wen-Yu Xi; Tian-Ru Wu; Cui-Na Jiao; Jin-Xing Liu 001; Ying-Lian Gao (ICIC (3), 2023)

Spatial Domain Identification Based on Graph Attention Denoising Auto-encoder. - Yue Gao; Dai-Jun Zhang; Cui-Na Jiao; Ying-Lian Gao; Jin-Xing Liu 001 (ICIC (3), 2023)

Identify Complex Higher-Order Associations Between Alzheimer's Disease Genes and Imaging Markers Through Improved Adaptive Sparse Multi-view Canonical Correlation Analysis. - Yi-Ming Wang; Xiang-Zhen Kong; Boxin Guan; Chun-Hou Zheng 001; Ying-Lian Gao (ICIC (3), 2023)

MSF-LRR: Multi-Similarity Information Fusion Through Low-Rank Representation to Predict Disease-Associated Microbes. - Jin-Xing Liu 001; Meng-Meng Yin; Ying-Lian Gao; Junliang Shang; Chun-Hou Zheng 001 (IEEE ACM Trans. Comput. Biol. Bioinform., 2023)

Non-Negative Low-Rank Representation With Similarity Correction for Cell Type Identification in scRNA-Seq Data. - Jin-Xing Liu 001; Dai-Jun Zhang; Jing-Xiu Zhao; Chun-Hou Zheng 001; Ying-Lian Gao (IEEE ACM Trans. Comput. Biol. Bioinform., 2023)

LDCMFC: Predicting Long Non-Coding RNA and Disease Association Using Collaborative Matrix Factorization Based on Correntropy. - Wen-Yu Xi; Feng Zhou 021; Ying-Lian Gao; Jin-Xing Liu 001; Chun-Hou Zheng 001 (IEEE ACM Trans. Comput. Biol. Bioinform., 2023)

NTBiRW: A Novel Neighbor Model Based on Two-Tier Bi-Random Walk for Predicting Potential Disease-Related Microbes. - Meng-Meng Yin; Ying-Lian Gao; Chun-Hou Zheng 001; Jin-Xing Liu 001 (IEEE J. Biomed. Health Informatics, 2023)

BRWMC: Predicting lncRNA-disease associations based on bi-random walk and matrix completion on disease and lncRNA networks. - Guozheng Zhang; Ying-Lian Gao (Comput. Biol. Chem., 2023)

MSGCA: Drug-Disease Associations Prediction Based on Multi-Similarities Graph Convolutional Autoencoder. - Ying Wang 143; Ying-Lian Gao; Juan Wang 003; Feng Li 033; Jin-Xing Liu 001 (IEEE J. Biomed. Health Informatics, 2023)

Identification of Disease-Associated MicroRNAs Via Locality-Constrained Linear Coding-Based Ensemble Learning. - Yi Shen; Ying-Lian Gao; Juan Wang 003; Boxin Guan; Jin-Xing Liu 001 (J. Comput. Biol., 2023)

A Graph Representation Approach Based on Light Gradient Boosting Machine for Predicting Drug-Disease Associations. - Ying Wang 143; Jin-Xing Liu 001; Juan Wang 003; Junliang Shang; Ying-Lian Gao (J. Comput. Biol., 2023)

BioSTD: A New Tensor Multi-View Framework via Combining Tensor Decomposition and Strong Complementarity Constraint for Analyzing Cancer Omics Data. - Ying-Lian Gao; Qian Qiao; Juan Wang 003; Shasha Yuan; Jin-Xing Liu 001 (IEEE J. Biomed. Health Informatics, 2023)

Integrating Local and Global Information to Decipher Spatial Domains of Spatial Transcriptomics by Attention-based Graph Convolutional Network. - Xu-Ran Dou; Yue Gao; Junliang Shang; Chun-Hou Zheng 001; Ying-Lian Gao; Jin-Xing Liu 001 (BIBM, 2024)

stMCFN: A Multi-view Contrastive Fusion Method for Spatial Domain Identification in Spatial Transcriptomics. - Jing Jing 001; Ying-Lian Gao; Yue Gao; Dao-Hui Ge; Chun-Hou Zheng 001; Jin-Xing Liu 001 (ICIC (LNBI 1), 2024)

Heterogeneous network and graph attention auto-encoder for LncRNA-disease association prediction. - Jin-Xing Liu 001; Wen-Yu Xi; Ling-Yun Dai; Chun-Hou Zheng 001; Ying-Lian Gao (CoRR, 2024)

LncRNA-disease association prediction method based on heterogeneous information completion and convolutional neural network. - Wen-Yu Xi; Juan Wang 003; Yu-Lin Zhang; Jin-Xing Liu 001; Ying-Lian Gao (CoRR, 2024)

Multi-modal imaging genetics data fusion by deep auto-encoder and self-representation network for Alzheimer's disease diagnosis and biomarkers extraction. - Cui-Na Jiao; Ying-Lian Gao; Dao-Hui Ge; Junliang Shang; Jin-Xing Liu 001 (Eng. Appl. Artif. Intell., 2024)

A review of recent advances in spatially resolved transcriptomics data analysis. - Yue Gao; Ying-Lian Gao; Jing Jing 001; Feng Li 033; Chun-Hou Zheng 001; Jin-Xing Liu 001 (Neurocomputing, 2024)

SLGCN: Structure-enhanced line graph convolutional network for predicting drug-disease associations. - Bao-Min Liu; Ying-Lian Gao; Feng Li 033; Chun-Hou Zheng 001; Jin-Xing Liu 001 (Knowl. Based Syst., 2024)

Multi-Kernel Graph Attention Deep Autoencoder for MiRNA-Disease Association Prediction. - Cui-Na Jiao; Feng Zhou 021; Bao-Min Liu; Chun-Hou Zheng 001; Jin-Xing Liu 001; Ying-Lian Gao (IEEE J. Biomed. Health Informatics, 2024)

A New Graph Autoencoder-Based Consensus-Guided Model for scRNA-seq Cell Type Detection. - Dai-Jun Zhang; Ying-Lian Gao; Jing-Xiu Zhao; Chun-Hou Zheng 001; Jin-Xing Liu 001 (IEEE Trans. Neural Networks Learn. Syst., 2024)

Diagnosis-Guided Deep Subspace Clustering Association Study for Pathogenetic Markers Identification of Alzheimer's Disease Based on Comparative Atlases. - Cui-Na Jiao; Junliang Shang; Feng Li 033; Xinchun Cui; Yan-Li Wang; Ying-Lian Gao; Jin-Xing Liu 001 (IEEE J. Biomed. Health Informatics, 2024)

KFDAE: CircRNA-Disease Associations Prediction Based on Kernel Fusion and Deep Auto-Encoder. - Wen-Yue Kang; Ying-Lian Gao; Ying Wang 143; Feng Li 033; Jin-Xing Liu 001 (IEEE J. Biomed. Health Informatics, 2024)

M3HOGAT: A Multi-View Multi-Modal Multi-Scale High-Order Graph Attention Network for Microbe-Disease Association Prediction. - Shuang Wang; Jin-Xing Liu 001; Feng Li 033; Juan Wang 003; Ying-Lian Gao (IEEE J. Biomed. Health Informatics, 2024)

stDGAC: A novel identifying spatial domains method via graph attention contrastive network for spatial transcriptomics data. - Jing Jing 001; Yue Gao; Ying-Lian Gao; Feng Li 033; Juan Wang 003; Jin-Xing Liu 001 (Comput. Biol. Medicine, 2025)

STDDAE: Identifying spatial domains in spatial transcriptomics by dual denoising autoencoder with attention mechanism. - Yue Gao; Ying-Lian Gao; Cui-Na Jiao; Xu-Ran Dou; Feng Li 033; Jin-Xin Liu 001 (Eng. Appl. Artif. Intell., 2025)

SAMGCN: A spatially-augmented multi-view graph convolutional network for identifying spatial domains. - Hao Liu 075; Yue Gao; Ying-Lian Gao; Cui-Na Jiao; Junliang Shang; Jin-Xing Liu (Eng. Appl. Artif. Intell., 2025)

SpaMGAN: Multi-view graph augmentation network for spatial domain identification in spatial transcriptomics. - Hao Liu 075; Yue Gao; Cui-Na Jiao; Chun-Hou Zheng 001; Ying-Lian Gao; Jin-Xing Liu 001; Yan-Li Wang (Knowl. Based Syst., 2025)