Attribute Clustering for Grouping, Selection, and Classification of Gene Expression Data

2005 ◽  
Vol 2 (2) ◽  
pp. 83-101 ◽  
Author(s):  
Wai-Ho Au ◽  
K.C.C. Chan ◽  
A.K.C. Wong ◽  
Yang Wang
Keyword(s):  
Gene Expression ◽  
Gene Expression Data ◽  
Expression Data ◽  
Attribute Clustering

scholarly journals Rough Set based Attribute Clustering for Sample Classification of Gene Expression Data

2012 ◽  
Vol 38 ◽  
pp. 1788-1792 ◽  
Author(s):  
Rudra Kalyan Nayak ◽  
Debahuti Mishra ◽  
Kailash Shaw ◽  
Sashikala Mishra
Keyword(s):  
Gene Expression ◽  
Gene Expression Data ◽  
Rough Set ◽  
Expression Data ◽  
Sample Classification ◽  
Attribute Clustering

COMBINING GENERALIZED NMF AND DISCRIMINATIVE MIXTURE MODELS FOR CLASSIFICATION OF GENE EXPRESSION DATA

2008 ◽  
Vol 22 (08) ◽  
pp. 1587-1598 ◽  
Author(s):  
WEIXIANG LIU ◽  
KEHONG YUAN ◽  
JIAN WU ◽  
DATIAN YE ◽  
ZHEN JI ◽  
...  
Keyword(s):  
Gene Expression ◽  
Mixture Model ◽  
Gene Expression Data ◽  
Small Sample Size ◽  
Data Classification ◽  
Small Sample ◽  
Training Data ◽  
Microarray Data Analysis ◽  
Expression Data

Classification of gene expression samples is a core task in microarray data analysis. How to reduce thousands of genes and to select a suitable classifier are two key issues for gene expression data classification. This paper introduces a framework on combining both feature extraction and classifier simultaneously. Considering the non-negativity, high dimensionality and small sample size, we apply a discriminative mixture model which is designed for non-negative gene express data classification via non-negative matrix factorization (NMF) for dimension reduction. In order to enhance the sparseness of training data for fast learning of the mixture model, a generalized NMF is also adopted. Experimental results on several real gene expression datasets show that the classification accuracy, stability and decision quality can be significantly improved by using the generalized method, and the proposed method can give better performance than some previous reported results on the same datasets.

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