scholarly journals Knowledge-based gene expression classification via matrix factorization

2008 ◽  
Vol 24 (15) ◽  
pp. 1688-1697 ◽  
Author(s):  
R. Schachtner ◽  
D. Lutter ◽  
P. Knollmüller ◽  
A. M. Tomé ◽  
F. J. Theis ◽  
...  
Keyword(s):  
Gene Expression ◽  
Matrix Factorization ◽  
Gene Expression Classification ◽  
Knowledge Based ◽  
Expression Classification

scholarly journals Pre-processing for noise detection in gene expression classification data

2009 ◽  
Vol 15 (1) ◽  
pp. 3-11 ◽  
Author(s):  
Giampaolo Luiz Libralon ◽  
André Carlos Ponce de Leon Ferreira de Carvalho ◽  
Ana Carolina Lorena
Keyword(s):  
Gene Expression ◽  
Noise Detection ◽  
Gene Expression Classification ◽  
Expression Classification

ELM-based gene expression classification with misclassification cost

2013 ◽  
Vol 25 (3-4) ◽  
pp. 525-531 ◽  
Author(s):  
Hui-juan Lu ◽  
En-hui Zheng ◽  
Yi Lu ◽  
Xiao-ping Ma ◽  
Jin-yong Liu
Keyword(s):  
Gene Expression ◽  
Misclassification Cost ◽  
Gene Expression Classification ◽  
Expression Classification

scholarly journals Two Efficient Algorithms for Orthogonal Nonnegative Matrix Factorization

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Jing Wu ◽  
Bin Chen ◽  
Tao Han
Keyword(s):  
Gene Expression ◽  
Matrix Factorization ◽  
Nonnegative Matrix Factorization ◽  
State Of The Art ◽  
Nonnegative Matrix ◽  
Alternating Direction Method ◽  
Data Matrix ◽  
Popular Method ◽  
Alternating Direction ◽  
Expression Classification

Nonnegative matrix factorization (NMF) is a popular method for the multivariate analysis of nonnegative data. It involves decomposing a data matrix into a product of two factor matrices with all entries restricted to being nonnegative. Orthogonal nonnegative matrix factorization (ONMF) has been introduced recently. This method has demonstrated remarkable performance in clustering tasks, such as gene expression classification. In this study, we introduce two convergence methods for solving ONMF. First, we design a convergent orthogonal algorithm based on the Lagrange multiplier method. Second, we propose an approach that is based on the alternating direction method. Finally, we demonstrate that the two proposed approaches tend to deliver higher-quality solutions and perform better in clustering tasks compared with a state-of-the-art ONMF.

scholarly journals Pre-processing for noise detection in gene expression classification data

2009 ◽  
Vol 15 (1) ◽  
pp. 3-11
Author(s):  
Giampaolo Luiz Libralon ◽  
André Carlos Ponce de Leon Ferreira Carvalho ◽  
Ana Carolina Lorena
Keyword(s):  
Gene Expression ◽  
Noise Detection ◽  
Gene Expression Classification ◽  
Expression Classification
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