Cargando…
An NMF-L(2,1)-Norm Constraint Method for Characteristic Gene Selection
Recent research has demonstrated that characteristic gene selection based on gene expression data remains faced with considerable challenges. This is primarily because gene expression data are typically high dimensional, negative, non-sparse and noisy. However, existing methods for data analysis are...
Autores principales: | Wang, Dong, Liu, Jin-Xing, Gao, Ying-Lian, Yu, Jiguo, Zheng, Chun-Hou, Xu, Yong |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2016
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4948826/ https://www.ncbi.nlm.nih.gov/pubmed/27428058 http://dx.doi.org/10.1371/journal.pone.0158494 |
Ejemplares similares
-
Joint L(1/2)-Norm Constraint and Graph-Laplacian PCA Method for Feature Extraction
por: Feng, Chun-Mei, et al.
Publicado: (2017) -
Co-differential Gene Selection and Clustering Based on Graph Regularized Multi-View NMF in Cancer Genomic Data
por: Yu, Na, et al.
Publicado: (2018) -
Improved Image Fusion Method Based on NSCT and Accelerated NMF
por: Wang, Juan, et al.
Publicado: (2012) -
Theorems on Positive Data: On the Uniqueness of NMF
por: Laurberg, Hans, et al.
Publicado: (2008) -
Joint Lp-Norm and L(2,1)-Norm Constrained Graph Laplacian PCA for Robust Tumor Sample Clustering and Gene Network Module Discovery
por: Kong, Xiang-Zhen, et al.
Publicado: (2021)