Cargando…
Non-Negative Symmetric Low-Rank Representation Graph Regularized Method for Cancer Clustering Based on Score Function
As an important approach to cancer classification, cancer sample clustering is of particular importance for cancer research. For high dimensional gene expression data, examining approaches to selecting characteristic genes with high identification for cancer sample clustering is an important researc...
Autores principales: | Lu, Conghai, Wang, Juan, Liu, Jinxing, Zheng, Chunhou, Kong, Xiangzhen, Zhang, Xiaofeng |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2020
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6987458/ https://www.ncbi.nlm.nih.gov/pubmed/32038712 http://dx.doi.org/10.3389/fgene.2019.01353 |
Ejemplares similares
-
Multi-cancer samples clustering via graph regularized low-rank representation method under sparse and symmetric constraints
por: Wang, Juan, et al.
Publicado: (2019) -
Zero-symmetric graphs: trivalent graphical regular representations of groups
por: Coxeter, H S M, et al.
Publicado: (1981) -
A truncated nuclear norm and graph-Laplacian regularized low-rank representation method for tumor clustering and gene selection
por: Liu, Qi
Publicado: (2022) -
Multi-view manifold regularized compact low-rank representation for cancer samples clustering on multi-omics data
por: Wang, Juan, et al.
Publicado: (2022) -
Gene Feature Extraction Based on Nonnegative Dual Graph Regularized Latent Low-Rank Representation
por: Yang, Guoliang, et al.
Publicado: (2017)