HTRPCA: Hypergraph Regularized Tensor Robust Principal Component Analysis for Sample Clustering in Tumor Omics Data-Reference-Cited by-同舟云学术

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HTRPCA: Hypergraph Regularized Tensor Robust Principal Component Analysis for Sample Clustering in Tumor Omics Data

Published:2021-06-11 Issue: Volume: Page:
ISSN:1913-2751
Container-title:Interdisciplinary Sciences: Computational Life Sciences
language:en
Short-container-title:Interdiscip Sci Comput Life Sci

Author:

Zhao Yu-Ying,Jiao Cui-Na,Wang Mao-Li,Liu Jin-Xing,Wang Juan,Zheng Chun-Hou

Funder

National Natural Science Foundation of China

Publisher

Springer Science and Business Media LLC

Subject

Health Informatics,Computer Science Applications,General Biochemistry, Genetics and Molecular Biology

Link

https://link.springer.com/content/pdf/10.1007/s12539-021-00441-8.pdf

Reference33 articles.

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2. Bray F, Ferlay J, Soerjomataram I, Siegel RL, Torre LA, Jemal A (2018) Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA 68(6):394–424. https://doi.org/10.3322/caac.21492

3. Liu JX, Gao YL, Zheng CH, Xu Y, Yu J (2016) Block-constraint robust principal component analysis and its application to integrated analysis of TCGA data. IEEE Trans Nanobiosci 15(6):510–516. https://doi.org/10.1109/TNB.2016.2574923

4. Wold S, Esbensen K, Geladi P (1987) Principal component analysis. Chemom Intell Lab Syst 2(1–3):37–52. https://doi.org/10.1016/0169-7439(87)80084-9

5. Chun-Mei F, Ying-Lian G, Jin-Xing L, Juan W, Dong-Qin W, Chang-Gang W (2017) Joint L1/2-norm constraint and graph-Laplacian PCA method for feature extraction. Biomed Res Int 2017:5073427. https://doi.org/10.1155/2017/5073427

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