154 related articles for article (PubMed ID: 28692983)
1. Robust Principal Component Analysis Regularized by Truncated Nuclear Norm for Identifying Differentially Expressed Genes.
Wang YX; Gao YL; Liu JX; Kong XZ; Li HJ
IEEE Trans Nanobioscience; 2017 Sep; 16(6):447-454. PubMed ID: 28692983
[TBL] [Abstract][Full Text] [Related]
2. Differentially expressed genes selection via Laplacian regularized low-rank representation method.
Wang YX; Liu JX; Gao YL; Zheng CH; Shang JL
Comput Biol Chem; 2016 Dec; 65():185-192. PubMed ID: 27693191
[TBL] [Abstract][Full Text] [Related]
3. Tensor Robust Principal Component Analysis with a New Tensor Nuclear Norm.
Lu C; Feng J; Chen Y; Liu W; Lin Z; Yan S
IEEE Trans Pattern Anal Mach Intell; 2020 Apr; 42(4):925-938. PubMed ID: 30629495
[TBL] [Abstract][Full Text] [Related]
4. A Mixed-Norm Laplacian Regularized Low-Rank Representation Method for Tumor Samples Clustering.
Wang J; Liu JX; Zheng CH; Wang YX; Kong XZ; Wen CG
IEEE/ACM Trans Comput Biol Bioinform; 2019; 16(1):172-182. PubMed ID: 29990217
[TBL] [Abstract][Full Text] [Related]
5. Ranking analysis for identifying differentially expressed genes.
Qi Y; Sun H; Sun Q; Pan L
Genomics; 2011 May; 97(5):326-9. PubMed ID: 21402142
[TBL] [Abstract][Full Text] [Related]
6. A P-Norm Robust Feature Extraction Method for Identifying Differentially Expressed Genes.
Liu J; Liu JX; Gao YL; Kong XZ; Wang XS; Wang D
PLoS One; 2015; 10(7):e0133124. PubMed ID: 26201006
[TBL] [Abstract][Full Text] [Related]
7. Improved robust tensor principal component analysis for accelerating dynamic MR imaging reconstruction.
Jiang M; Shen Q; Li Y; Yang X; Zhang J; Wang Y; Xia L
Med Biol Eng Comput; 2020 Jul; 58(7):1483-1498. PubMed ID: 32372326
[TBL] [Abstract][Full Text] [Related]
8. DSTPCA: Double-Sparse Constrained Tensor Principal Component Analysis Method for Feature Selection.
Hu Y; Liu JX; Gao YL; Shang J
IEEE/ACM Trans Comput Biol Bioinform; 2021; 18(4):1481-1491. PubMed ID: 31562100
[TBL] [Abstract][Full Text] [Related]
9. Inductive robust principal component analysis.
Bao BK; Liu G; Xu C; Yan S
IEEE Trans Image Process; 2012 Aug; 21(8):3794-800. PubMed ID: 22481823
[TBL] [Abstract][Full Text] [Related]
10. Robust PCA based method for discovering differentially expressed genes.
Liu JX; Wang YT; Zheng CH; Sha W; Mi JX; Xu Y
BMC Bioinformatics; 2013; 14 Suppl 8(Suppl 8):S3. PubMed ID: 23815087
[TBL] [Abstract][Full Text] [Related]
11. Fast and accurate matrix completion via truncated nuclear norm regularization.
Hu Y; Zhang D; Ye J; Li X; He X
IEEE Trans Pattern Anal Mach Intell; 2013 Sep; 35(9):2117-30. PubMed ID: 23868774
[TBL] [Abstract][Full Text] [Related]
12. A truncated nuclear norm and graph-Laplacian regularized low-rank representation method for tumor clustering and gene selection.
Liu Q
BMC Bioinformatics; 2022 Jan; 22(Suppl 12):436. PubMed ID: 35057728
[TBL] [Abstract][Full Text] [Related]
13. Robust and Efficient Biomolecular Clustering of Tumor Based on ${p}$ -Norm Singular Value Decomposition.
Kong XZ; Liu JX; Zheng CH; Hou MX; Wang J
IEEE Trans Nanobioscience; 2017 Jul; 16(5):341-348. PubMed ID: 28541216
[TBL] [Abstract][Full Text] [Related]
14. Recovering low-rank and sparse matrix based on the truncated nuclear norm.
Cao F; Chen J; Ye H; Zhao J; Zhou Z
Neural Netw; 2017 Jan; 85():10-20. PubMed ID: 27814461
[TBL] [Abstract][Full Text] [Related]
15. A Class-Information-Based Sparse Component Analysis Method to Identify Differentially Expressed Genes on RNA-Seq Data.
Liu JX; Xu Y; Gao YL; Zheng CH; Wang D; Zhu Q
IEEE/ACM Trans Comput Biol Bioinform; 2016; 13(2):392-8. PubMed ID: 27045835
[TBL] [Abstract][Full Text] [Related]
16. Low-Rank Approximation via Generalized Reweighted Iterative Nuclear and Frobenius Norms.
Huang Y; Liao G; Xiang Y; Zhang L; Li J; Nehorai A
IEEE Trans Image Process; 2019 Oct; ():. PubMed ID: 31675328
[TBL] [Abstract][Full Text] [Related]
17. A Truncated Nuclear Norm Regularization Method Based on Weighted Residual Error for Matrix Completion.
Qing Liu ; Zhihui Lai ; Zongwei Zhou ; Fangjun Kuang ; Zhong Jin
IEEE Trans Image Process; 2016 Jan; 25(1):316-30. PubMed ID: 26625414
[TBL] [Abstract][Full Text] [Related]
18. Truncated Robust Principal Component Analysis and Noise Reduction for Single Cell RNA Sequencing Data.
Gogolewski K; Sykulski M; Chung NC; Gambin A
J Comput Biol; 2019 Aug; 26(8):782-793. PubMed ID: 31045436
[No Abstract] [Full Text] [Related]
19. Double nuclear norm-based matrix decomposition for occluded image recovery and background modeling.
Zhang F; Yang J; Tai Y; Tang J
IEEE Trans Image Process; 2015 Jun; 24(6):1956-66. PubMed ID: 25667350
[TBL] [Abstract][Full Text] [Related]
20. Sparse p-norm Nonnegative Matrix Factorization for clustering gene expression data.
Liu W; Yuan K
Int J Data Min Bioinform; 2008; 2(3):236-49. PubMed ID: 19024496
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]