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2020 – today
- 2024
[j56]Cui-Na Jiao
, Ying-Lian Gao, Dao-Hui Ge, Junliang Shang, Jin-Xing Liu:
Multi-modal imaging genetics data fusion by deep auto-encoder and self-representation network for Alzheimer's disease diagnosis and biomarkers extraction. Eng. Appl. Artif. Intell. 130: 107782 (2024)- [j55]Yue Gao, Ying-Lian Gao, Jing Jing, Feng Li, Chun-Hou Zheng, Jin-Xing Liu:
A review of recent advances in spatially resolved transcriptomics data analysis. Neurocomputing 603: 128283 (2024) - [j54]Bao-Min Liu, Ying-Lian Gao, Feng Li, Chun-Hou Zheng, Jin-Xing Liu:
SLGCN: Structure-enhanced line graph convolutional network for predicting drug-disease associations. Knowl. Based Syst. 283: 111187 (2024) - [j53]Cui-Na Jiao, Feng Zhou, Bao-Min Liu, Chun-Hou Zheng, Jin-Xing Liu, Ying-Lian Gao:
Multi-Kernel Graph Attention Deep Autoencoder for MiRNA-Disease Association Prediction. IEEE J. Biomed. Health Informatics 28(2): 1110-1121 (2024) - [j52]Cui-Na Jiao, Junliang Shang, Feng Li, Xinchun Cui, Yan-Li Wang, Ying-Lian Gao, Jin-Xing Liu:
Diagnosis-Guided Deep Subspace Clustering Association Study for Pathogenetic Markers Identification of Alzheimer's Disease Based on Comparative Atlases. IEEE J. Biomed. Health Informatics 28(5): 3029-3041 (2024) - [j51]Wen-Yue Kang, Ying-Lian Gao, Ying Wang, Feng Li, Jin-Xing Liu:
KFDAE: CircRNA-Disease Associations Prediction Based on Kernel Fusion and Deep Auto-Encoder. IEEE J. Biomed. Health Informatics 28(5): 3178-3185 (2024) - [j50]Shuang Wang, Jin-Xing Liu, Feng Li, Juan Wang, Ying-Lian Gao:
M3HOGAT: A Multi-View Multi-Modal Multi-Scale High-Order Graph Attention Network for Microbe-Disease Association Prediction. IEEE J. Biomed. Health Informatics 28(10): 6259-6267 (2024) - [j49]Dai-Jun Zhang, Ying-Lian Gao, Jing-Xiu Zhao, Chun-Hou Zheng, Jin-Xing Liu:
A New Graph Autoencoder-Based Consensus-Guided Model for scRNA-seq Cell Type Detection. IEEE Trans. Neural Networks Learn. Syst. 35(2): 2473-2483 (2024) - [c35]Jing Jing, Ying-Lian Gao, Yue Gao, Dao-Hui Ge, Chun-Hou Zheng, Jin-Xing Liu:
stMCFN: A Multi-view Contrastive Fusion Method for Spatial Domain Identification in Spatial Transcriptomics. ICIC (LNBI 1) 2024: 321-331 - [i3]Jin-Xing Liu, Wen-Yu Xi, Ling-Yun Dai, Chun-Hou Zheng, Ying-Lian Gao:
Heterogeneous network and graph attention auto-encoder for LncRNA-disease association prediction. CoRR abs/2405.02354 (2024) - [i2]Wen-Yu Xi, Juan Wang, Yu-Lin Zhang, Jin-Xing Liu, Ying-Lian Gao:
LncRNA-disease association prediction method based on heterogeneous information completion and convolutional neural network. CoRR abs/2406.03406 (2024) - 2023
- [j48]Guozheng Zhang, Ying-Lian Gao:
BRWMC: Predicting lncRNA-disease associations based on bi-random walk and matrix completion on disease and lncRNA networks. Comput. Biol. Chem. 103: 107833 (2023) - [j47]Yi Shen, Ying-Lian Gao, Juan Wang, Boxin Guan, Jin-Xing Liu:
Identification of Disease-Associated MicroRNAs Via Locality-Constrained Linear Coding-Based Ensemble Learning. J. Comput. Biol. 30(8): 926-936 (2023) - [j46]Ying Wang, Jin-Xing Liu, Juan Wang, Junliang Shang, Ying-Lian Gao:
A Graph Representation Approach Based on Light Gradient Boosting Machine for Predicting Drug-Disease Associations. J. Comput. Biol. 30(8): 937-947 (2023) - [j45]Jin-Xing Liu, Meng-Meng Yin, Ying-Lian Gao, Junliang Shang, Chun-Hou Zheng:
MSF-LRR: Multi-Similarity Information Fusion Through Low-Rank Representation to Predict Disease-Associated Microbes. IEEE ACM Trans. Comput. Biol. Bioinform. 20(1): 534-543 (2023) - [j44]Wen-Yu Xi, Feng Zhou, Ying-Lian Gao, Jin-Xing Liu, Chun-Hou Zheng:
LDCMFC: Predicting Long Non-Coding RNA and Disease Association Using Collaborative Matrix Factorization Based on Correntropy. IEEE ACM Trans. Comput. Biol. Bioinform. 20(3): 1774-1782 (2023) - [j43]Jin-Xing Liu, Dai-Jun Zhang, Jing-Xiu Zhao, Chun-Hou Zheng, Ying-Lian Gao:
Non-Negative Low-Rank Representation With Similarity Correction for Cell Type Identification in scRNA-Seq Data. IEEE ACM Trans. Comput. Biol. Bioinform. 20(6): 3737-3747 (2023) - [j42]Meng-Meng Yin, Ying-Lian Gao, Chun-Hou Zheng, Jin-Xing Liu:
NTBiRW: A Novel Neighbor Model Based on Two-Tier Bi-Random Walk for Predicting Potential Disease-Related Microbes. IEEE J. Biomed. Health Informatics 27(3): 1644-1653 (2023) - [j41]Ying Wang, Ying-Lian Gao, Juan Wang, Feng Li, Jin-Xing Liu:
MSGCA: Drug-Disease Associations Prediction Based on Multi-Similarities Graph Convolutional Autoencoder. IEEE J. Biomed. Health Informatics 27(7): 3686-3694 (2023) - [j40]Ying-Lian Gao, Qian Qiao, Juan Wang, Shasha Yuan, Jin-Xing Liu:
BioSTD: A New Tensor Multi-View Framework via Combining Tensor Decomposition and Strong Complementarity Constraint for Analyzing Cancer Omics Data. IEEE J. Biomed. Health Informatics 27(10): 5187-5198 (2023) - [c34]Wen-Yue Kang, Chun-Hou Zheng, Ying-Lian Gao, Juan Wang, Junliang Shang, Jin-Xing Liu:
GRPGAT: Predicting CircRNA-disease Associations Based on Graph Random Propagation Network and Graph Attention Network. BIBM 2023: 233-236 - [c33]Shuang Wang, Jin-Xing Liu, Bao-Min Liu, Ling-Yun Dai, Feng Li, Ying-Lian Gao:
MKGSAGE: A Computational Framework via Multiple Kernel Fusion on GraphSAGE for Inferring Potential Disease-Related Microbes. BIBM 2023: 648-653 - [c32]Xu-Ran Dou, Wen-Yu Xi, Tian-Ru Wu, Cui-Na Jiao, Jin-Xing Liu, Ying-Lian Gao:
LANCMDA: Predicting MiRNA-Disease Associations via LightGBM with Attributed Network Construction. ICIC (3) 2023: 291-299 - [c31]Yi-Ming Wang, Xiang-Zhen Kong, Boxin Guan, Chun-Hou Zheng, Ying-Lian Gao:
Identify Complex Higher-Order Associations Between Alzheimer's Disease Genes and Imaging Markers Through Improved Adaptive Sparse Multi-view Canonical Correlation Analysis. ICIC (3) 2023: 324-334 - [c30]Yue Gao, Dai-Jun Zhang, Cui-Na Jiao, Ying-Lian Gao, Jin-Xing Liu:
Spatial Domain Identification Based on Graph Attention Denoising Auto-encoder. ICIC (3) 2023: 359-367 - 2022
- [j39]Bao-Min Liu, Ying-Lian Gao, Dai-Jun Zhang, Feng Zhou, Juan Wang, Chun-Hou Zheng, Jin-Xing Liu:
A new framework for drug-disease association prediction combing light-gated message passing neural network and gated fusion mechanism. Briefings Bioinform. 23(6) (2022) - [j38]Meng-Meng Yin, Ying-Lian Gao, Junliang Shang, Chun-Hou Zheng, Jin-Xing Liu:
Multi-similarity fusion-based label propagation for predicting microbes potentially associated with diseases. Future Gener. Comput. Syst. 134: 247-255 (2022) - [j37]Hang-Jin Yang, Yuxia Lei, Juan Wang, Xiang-Zhen Kong, Jin-Xing Liu, Ying-Lian Gao:
Tensor decomposition based on the potential low-rank and p-shrinkage generalized threshold algorithm for analyzing cancer multiomics data. J. Bioinform. Comput. Biol. 20(2): 2250002:1-2250002:20 (2022) - [j36]Liang-Rui Ren, Ying-Lian Gao, Junliang Shang, Jin-Xing Liu:
Kernel risk-sensitive mean p-power error based robust extreme learning machine for classification. Int. J. Mach. Learn. Cybern. 13(1): 199-216 (2022) - [j35]Chuan-Yuan Wang, Ying-Lian Gao, Jin-Xing Liu, Xiang-Zhen Kong, Chun-Hou Zheng:
Single-Cell RNA Sequencing Data Clustering by Low-Rank Subspace Ensemble Framework. IEEE ACM Trans. Comput. Biol. Bioinform. 19(2): 1154-1164 (2022) - [j34]Ying-Lian Gao, Ming-Juan Wu, Jin-Xing Liu, Chun-Hou Zheng, Juan Wang:
Robust Principal Component Analysis Based On Hypergraph Regularization for Sample Clustering and Co-Characteristic Gene Selection. IEEE ACM Trans. Comput. Biol. Bioinform. 19(4): 2420-2430 (2022) - [j33]Meng-Meng Yin, Jin-Xing Liu, Ying-Lian Gao, Xiang-Zhen Kong, Chun-Hou Zheng:
NCPLP: A Novel Approach for Predicting Microbe-Associated Diseases With Network Consistency Projection and Label Propagation. IEEE Trans. Cybern. 52(6): 5079-5087 (2022) - [j32]Chuan-Yuan Wang, Ying-Lian Gao, Xiang-Zhen Kong, Jin-Xing Liu, Chun-Hou Zheng:
Unsupervised Cluster Analysis and Gene Marker Extraction of scRNA-seq Data Based On Non-Negative Matrix Factorization. IEEE J. Biomed. Health Informatics 26(1): 458-467 (2022) - [c29]Wen-Yu Xi, Qianqian Ren, Jin-Xing Liu, Ying-Lian Gao:
HSAELDA: Predicting lncRNA-disease associations based on heterogeneous networks and Stacked Autoencoder. BIBM 2022: 607-612 - [c28]Zhi-Yuan Li, Ying-Lian Gao, Zhen-Xin Niu, Shasha Yuan, Chun-Hou Zheng, Jin-Xing Liu:
An integrated Extreme learning machine based on kernel risk-sensitive loss of q-Gaussian and voting mechanism for sample classification. BIBM 2022: 2088-2094 - [c27]Guozheng Zhang, Shu-Zhen Li, Xu-Ran Dou, Junliang Shang, Qianqian Ren, Ying-Lian Gao:
Predicting LncRNA-Disease Associations Based on LncRNA-MiRNA-Disease Multilayer Association Network and Bipartite Network Recommendation. BIBM 2022: 2216-2223 - [c26]Ying Wang, Ying-Lian Gao, Juan Wang, Junliang Shang, Jin-Xing Liu:
MLMVFE: A Machine Learning Approach Based on Muli-view Features Extraction for Drug-Disease Associations Prediction. ISBRA 2022: 1-8 - [c25]Yi Shen, Ying-Lian Gao, Shu-Zhen Li, Boxin Guan, Jin-Xing Liu:
A Locality-Constrained Linear Coding-Based Ensemble Learning Framework for Predicting Potentially Disease-Associated MiRNAs. ISBRA 2022: 295-302 - 2021
- [j31]Jin-Xing Liu, Ming-Ming Gao, Zhen Cui, Ying-Lian Gao, Feng Li:
DSCMF: prediction of LncRNA-disease associations based on dual sparse collaborative matrix factorization. BMC Bioinform. 22-S(3): 241 (2021) - [j30]Chuan-Yuan Wang, Ying-Lian Gao, Jin-Xing Liu, Ling-Yun Dai, Junliang Shang:
Sparse robust graph-regularized non-negative matrix factorization based on correntropy. J. Bioinform. Comput. Biol. 19(1): 2050047:1-2050047:24 (2021) - [j29]Liang-Rui Ren, Jin-Xing Liu, Ying-Lian Gao, Xiang-Zhen Kong, Chun-Hou Zheng:
Kernel Risk-Sensitive Loss based Hyper-graph Regularized Robust Extreme Learning Machine and Its Semi-supervised Extension for Classification. Knowl. Based Syst. 227: 107226 (2021) - [j28]Meng-Meng Yin, Zhen Cui, Ming-Ming Gao, Jin-Xing Liu, Ying-Lian Gao:
LWPCMF: Logistic Weighted Profile-Based Collaborative Matrix Factorization for Predicting MiRNA-Disease Associations. IEEE ACM Trans. Comput. Biol. Bioinform. 18(3): 1122-1129 (2021) - [j27]Yue Hu, Jin-Xing Liu, Ying-Lian Gao, Junliang Shang:
DSTPCA: Double-Sparse Constrained Tensor Principal Component Analysis Method for Feature Selection. IEEE ACM Trans. Comput. Biol. Bioinform. 18(4): 1481-1491 (2021) - [j26]Chuan-Yuan Wang, Na Yu, Ming-Juan Wu, Ying-Lian Gao, Jin-Xing Liu, Juan Wang:
Dual Hyper-Graph Regularized Supervised NMF for Selecting Differentially Expressed Genes and Tumor Classification. IEEE ACM Trans. Comput. Biol. Bioinform. 18(6): 2375-2383 (2021) - [j25]Jin-Xing Liu, Zhen Cui, Ying-Lian Gao, Xiang-Zhen Kong:
WGRCMF: A Weighted Graph Regularized Collaborative Matrix Factorization Method for Predicting Novel LncRNA-Disease Associations. IEEE J. Biomed. Health Informatics 25(1): 257-265 (2021) - [j24]Ming-Ming Gao, Zhen Cui, Ying-Lian Gao, Juan Wang, Jin-Xing Liu:
Multi-Label Fusion Collaborative Matrix Factorization for Predicting LncRNA-Disease Associations. IEEE J. Biomed. Health Informatics 25(3): 881-890 (2021) - [c24]Cui-Na Jiao, Jin-Xing Liu, Ying-Lian Gao, Xiang-Zhen Kong, Chun-Hou Zheng, Xianzi Yu:
Sparse Hyper-graph Non-negative Matrix Factorization by Maximizing Correntropy. BIBM 2021: 418-423 - [c23]Qian Qiao, Ying-Lian Gao, Shasha Yuan, Jin-Xing Liu:
Robust Tensor Method Based on Correntropy and Tensor Singular Value Decomposition for Cancer Genomics Data. BIBM 2021: 509-514 - [c22]Dai-Jun Zhang, Jing-Xiu Zhao, Jin-Xing Liu, Ying-Lian Gao:
Adaptive total-variation joint learning model for analyzing single cell RNA seq data. BIBM 2021: 775-778 - [c21]Zhen-Xin Niu, Liang-Rui Ren, Rong Zhu, Xiang-Zhen Kong, Ying-Lian Gao, Jin-Xing Liu:
Extreme Learning Machine Based on Double Kernel Risk-Sensitive Loss for Cancer Samples Classification. ICIC (2) 2021: 532-539 - [c20]Ying-Lian Gao, Meng-Meng Yin, Jin-Xing Liu, Junliang Shang, Chun-Hou Zheng:
MKL-LP: Predicting Disease-Associated Microbes with Multiple-Similarity Kernel Learning-Based Label Propagation. ISBRA 2021: 3-10 - 2020
- [j23]Liang-Rui Ren, Ying-Lian Gao, Jin-Xing Liu, Junliang Shang, Chun-Hou Zheng:
Correntropy induced loss based sparse robust graph regularized extreme learning machine for cancer classification. BMC Bioinform. 21(1): 445 (2020) - [j22]Tian-Ru Wu, Meng-Meng Yin, Cui-Na Jiao, Ying-Lian Gao, Xiang-Zhen Kong, Jin-Xing Liu:
MCCMF: collaborative matrix factorization based on matrix completion for predicting miRNA-disease associations. BMC Bioinform. 21(1): 454 (2020) - [j21]Liang-Rui Ren, Ying-Lian Gao, Jin-Xing Liu, Rong Zhu, Xiang-Zhen Kong:
L2, 1-Extreme Learning Machine: An Efficient Robust Classifier for Tumor Classification. Comput. Biol. Chem. 89: 107368 (2020) - [j20]Yao Lu, Ying-Lian Gao, Pei-Yong Li, Jin-Xing Liu:
A multi-view classification and feature selection method via sparse low-rank regression analysis. Int. J. Data Min. Bioinform. 24(2): 140-159 (2020) - [j19]Zhen Cui, Jin-Xing Liu, Ying-Lian Gao, Rong Zhu, Shasha Yuan:
LncRNA-Disease Associations Prediction Using Bipartite Local Model With Nearest Profile-Based Association Inferring. IEEE J. Biomed. Health Informatics 24(5): 1519-1527 (2020) - [j18]Ming-Juan Wu, Ying-Lian Gao, Jin-Xing Liu, Chun-Hou Zheng, Juan Wang:
Integrative Hypergraph Regularization Principal Component Analysis for Sample Clustering and Co-Expression Genes Network Analysis on Multi-Omics Data. IEEE J. Biomed. Health Informatics 24(6): 1823-1834 (2020) - [j17]Cui-Na Jiao, Ying-Lian Gao, Na Yu, Jin-Xing Liu, Lianyong Qi:
Hyper-Graph Regularized Constrained NMF for Selecting Differentially Expressed Genes and Tumor Classification. IEEE J. Biomed. Health Informatics 24(10): 3002-3011 (2020) - [c19]Chuan-Yuan Wang, Ying-Lian Gao, Cui-Na Jiao, Jin-Xing Liu, Chunhou Zheng, Xiang-Zhen Kong:
Locally Manifold Non-negative Matrix Factorization Based on Centroid for scRNA-seq Data Analysis. BIBM 2020: 121-125 - [c18]Liang-Rui Ren, Jin-Xing Liu, Ying-Lian Gao, Xiang-Zhen Kong, Chun-Hou Zheng:
Robust Graph Regularized Extreme Learning Machine Auto Encoder and Its Application to Single-Cell Samples Classification. ICIC (2) 2020: 537-545
2010 – 2019
- 2019
- [j16]Ying-Lian Gao, Mi-Xiao Hou, Jin-Xing Liu, Xiang-Zhen Kong:
An Integrated Graph Regularized Non-Negative Matrix Factorization Model for Gene Co-Expression Network Analysis. IEEE Access 7: 126594-126602 (2019) - [j15]Zhen Cui, Ying-Lian Gao, Jin-Xing Liu, Juan Wang, Junliang Shang, Ling-Yun Dai:
The computational prediction of drug-disease interactions using the dual-network L2,1-CMF method. BMC Bioinform. 20(1): 5 (2019) - [j14]Ying-Lian Gao, Zhen Cui, Jin-Xing Liu, Juan Wang, Chun-Hou Zheng:
NPCMF: Nearest Profile-based Collaborative Matrix Factorization method for predicting miRNA-disease associations. BMC Bioinform. 20(1): 353:1-353:10 (2019) - [j13]Zhen Cui, Ying-Lian Gao, Jin-Xing Liu, Ling-Yun Dai, Shasha Yuan:
L2, 1-GRMF: an improved graph regularized matrix factorization method to predict drug-target interactions. BMC Bioinform. 20-S(8): 287:1-287:13 (2019) - [j12]Zhen Cui, Jin-Xing Liu, Ying-Lian Gao, Chun-Hou Zheng, Juan Wang:
RCMF: a robust collaborative matrix factorization method to predict miRNA-disease associations. BMC Bioinform. 20-S(25): 686 (2019) - [j11]Mi-Xiao Hou, Ying-Lian Gao, Jin-Xing Liu, Ling-Yun Dai, Xiang-Zhen Kong, Junliang Shang:
Network analysis based on low-rank method for mining information on integrated data of multi-cancers. Comput. Biol. Chem. 78: 468-473 (2019) - [j10]Yue Hu, Jin-Xing Liu, Ying-Lian Gao, Shengjun Li, Juan Wang:
Differentially Expressed Genes Extracted by the Tensor Robust Principal Component Analysis (TRPCA) Method. Complex. 2019: 6136245:1-6136245:13 (2019) - [j9]Yong-Jing Hao, Ying-Lian Gao, Mi-Xiao Hou, Ling-Yun Dai, Jin-Xing Liu:
Hypergraph Regularized Discriminative Nonnegative Matrix Factorization on Sample Classification and Co-Differentially Expressed Gene Selection. Complex. 2019: 7081674:1-7081674:12 (2019) - [j8]Chun-Mei Feng, Yong Xu, Jin-Xing Liu, Ying-Lian Gao, Chun-Hou Zheng:
Supervised Discriminative Sparse PCA for Com-Characteristic Gene Selection and Tumor Classification on Multiview Biological Data. IEEE Trans. Neural Networks Learn. Syst. 30(10): 2926-2937 (2019) - [c17]Ming-Ming Gao, Zhen Cui, Ying-Lian Gao, Feng Li, Jin-Xing Liu:
Dual Sparse Collaborative Matrix Factorization Method Based on Gaussian Kernel Function for Predicting LncRNA-Disease Associations. ICIC (3) 2019: 318-326 - [c16]Meng-Meng Yin, Zhen Cui, Jin-Xing Liu, Ying-Lian Gao, Xiang-Zhen Kong:
DSNPCMF: Predicting MiRNA-Disease Associations with Collaborative Matrix Factorization Based on Double Sparse and Nearest Profile. IDMB 2019: 196-208 - [i1]Chun-Mei Feng, Yong Xu, Jin-Xing Liu, Ying-Lian Gao, Chun-Hou Zheng:
Supervised Discriminative Sparse PCA for Com-Characteristic Gene Selection and Tumor Classification on Multiview Biological Data. CoRR abs/1905.11837 (2019) - 2018
- [j7]Jin-Xing Liu, Dong Wang, Ying-Lian Gao, Chun-Hou Zheng, Yong Xu, Jiguo Yu:
Regularized Non-Negative Matrix Factorization for Identifying Differentially Expressed Genes and Clustering Samples: A Survey. IEEE ACM Trans. Comput. Biol. Bioinform. 15(3): 974-987 (2018) - [c15]Na Yu, Ying-Lian Gao, Jin-Xing Liu, Juan Wang, Junliang Shang:
Hypergraph regularized NMF by L2, 1-norm for Clustering and Com-abnormal Expression Genes Selection. BIBM 2018: 578-582 - [c14]Mi-Xiao Hou, Jin-Xing Liu, Junliang Shang, Ying-Lian Gao, Xiang-Zhen Kong, Ling-Yun Dai:
Performance Analysis of Non-negative Matrix Factorization Methods on TCGA Data. ICIC (2) 2018: 407-418 - 2017
- [j6]Jin-Xing Liu, Dong-Qin Wang, Chun-Hou Zheng, Ying-Lian Gao, Sha-Sha Wu, Junliang Shang:
Identifying drug-pathway association pairs based on L2, 1-integrative penalized matrix decomposition. BMC Syst. Biol. 11(6): 63-73 (2017) - [j5]Xiu-Xiu Xu, Ying-Lian Gao, Jin-Xing Liu, Yaxuan Wang, Ling-Yun Dai, Xiang-Zhen Kong, Shasha Yuan:
A novel low-rank representation method for identifying differentially expressed genes. Int. J. Data Min. Bioinform. 19(3): 185-201 (2017) - [j4]Jin-Xing Liu, Dong Wang, Ying-Lian Gao, Chun-Hou Zheng, Junliang Shang, Feng Liu, Yong Xu:
A joint-L2, 1-norm-constraint-based semi-supervised feature extraction for RNA-Seq data analysis. Neurocomputing 228: 263-269 (2017) - [c13]Yaxuan Wang, Jin-Xing Liu, Ying-Lian Gao, Chun-Hou Zheng, Ling-Yun Dai:
Low-rank representation regularized by L2, 1-norm for identifying differentially expressed genes. BIBM 2017: 626-629 - [c12]Ming-Juan Wu, Jin-Xing Liu, Ying-Lian Gao, Xiangzhen Kong, Chun-Mei Feng:
Feature selection and clustering via robust graph-laplacian PCA based on capped L1-norm. BIBM 2017: 1741-1745 - [c11]Na Yu, Jin-Xing Liu, Ying-Lian Gao, Chun-Hou Zheng, Juan Wang, Ming-Juan Wu:
Graph regularized robust non-negative matrix factorization for clustering and selecting differentially expressed genes. BIBM 2017: 1752-1756 - 2016
- [j3]Yaxuan Wang, Jin-Xing Liu, Ying-Lian Gao, Chun-Hou Zheng, Junliang Shang:
Differentially expressed genes selection via Laplacian regularized low-rank representation method. Comput. Biol. Chem. 65: 185-192 (2016) - [j2]Jin-Xing Liu, Yong Xu, Ying-Lian Gao, Chun-Hou Zheng, Dong Wang, Qi Zhu:
A Class-Information-Based Sparse Component Analysis Method to Identify Differentially Expressed Genes on RNA-Seq Data. IEEE ACM Trans. Comput. Biol. Bioinform. 13(2): 392-398 (2016) - [j1]Dong Wang, Jin-Xing Liu, Ying-Lian Gao, Chun-Hou Zheng, Yong Xu:
Characteristic Gene Selection Based on Robust Graph Regularized Non-Negative Matrix Factorization. IEEE ACM Trans. Comput. Biol. Bioinform. 13(6): 1059-1067 (2016) - [c10]Dong-Qin Wang, Chun-Hou Zheng, Ying-Lian Gao, Jin-Xing Liu, Sha-Sha Wu, Junliang Shang:
L21-iPaD: An efficient method for drug-pathway association pairs inference. BIBM 2016: 664-669 - [c9]Chun-Mei Feng, Jin-Xing Liu, Ying-Lian Gao, Juan Wang, Dong-Qin Wang, Yong Du:
A graph-Laplacian PCA based on L1/2-norm constraint for characteristic gene selection. BIBM 2016: 1795-1799 - [c8]Yao Lu, Ying-Lian Gao, Jin-Xing Liu, Chang-Gang Wen, Yaxuan Wang, Jiguo Yu:
Characteristic gene selection via L2, 1-norm Sparse Principal Component Analysis. BIBM 2016: 1828-1833 - [c7]Yaxuan Wang, Jin-Xing Liu, Ying-Lian Gao, Xiangzhen Kong, Chun-Hou Zheng, Yong Du:
Differentially expressed genes selection via Truncated Nuclear Norm Regularization. BIBM 2016: 1851-1855 - [c6]Mi-Xiao Hou, Ying-Lian Gao, Jin-Xing Liu, Junliang Shang, Chun-Hou Zheng:
Comparison of Non-negative Matrix Factorization Methods for Clustering Genomic Data. ICIC (2) 2016: 290-299 - [c5]Chun-Mei Feng, Ying-Lian Gao, Jin-Xing Liu, Chun-Hou Zheng, Shengjun Li, Dong Wang:
A Simple Review of Sparse Principal Components Analysis. ICIC (2) 2016: 374-383 - 2015
- [c4]Ying-Lian Gao, Jin-Xing Liu, Chun-Hou Zheng, Shengjun Li, Yuxia Lei:
A Two-Stage Sparse Selection Method for Extracting Characteristic Genes. ICIC (2) 2015: 577-588 - [c3]Dong Wang, Ying-Lian Gao, Jin-Xing Liu, Jiguo Yu, Chang-Gang Wen:
Application of Graph Regularized Non-negative Matrix Factorization in Characteristic Gene Selection. ICIC (2) 2015: 601-611 - [c2]Chun-Xia Ma, Ying-Lian Gao, Dong Wang, Jian Liu, Jin-Xing Liu:
Graph Regularized Non-negative Matrix with L0-Constraints for Selecting Characteristic Genes. ICIC (2) 2015: 612-622 - [c1]Jin-Xing Liu, Yong Xu, Ying-Lian Gao, Dong Wang, Chun-Hou Zheng, Junliang Shang:
Semi-supervised Feature Extraction for RNA-Seq Data Analysis. ICIC (3) 2015: 679-685
Coauthor Index
[j56] [j55] [j54] [j53] [j52] [j51] [j50] [j49] [c35] [i3] [i2] [j47] [j46] [j45] [j44] [j43] [j42] [j41] [j40] [c34] [c33] [c32] [c30] [j39] [j38] [j37] [j36] [j35] [j34] [j33] [j32] [c29] [c28] [c26] [c25] [j31] [j30] [j29] [j28] [j27] [j26] [j25] [j24] [c24] [c23] [c22] [c21] [c20] [j23] [j22] [j21] [j20] [j19] [j18] [j17] [c19] [c18] [j16] [j15] [j14] [j13] [j12] [j11] [j10] [j9] [j8] [c17] [c16] [i1] [j7] [c15] [c14] [j6] [j5] [j4] [c13] [c12] [c11] [j3] [j2] [j1] [c10] [c9] [c8] [c7] [c6] [c5] [c4] [c3] [c2] [c1]
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Citation data
Add a list of citing articles from and to record detail pages.
load citations from opencitations.net
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OpenAlex data
Load additional information about publications from 
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last updated on 2024-11-27 21:22 CET by the dblp team
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