scholarly journals Gene Expression Data Classification Using Consensus Independent Component Analysis

2008 ◽  
Vol 6 (2) ◽  
pp. 74-82 ◽  
Author(s):  
Chun-Hou Zheng ◽  
De-Shuang Huang ◽  
Xiang-Zhen Kong ◽  
Xing-Ming Zhao
Keyword(s):  
Gene Expression ◽  
Independent Component Analysis ◽  
Gene Expression Data ◽  
Data Classification ◽  
Component Analysis ◽  
Independent Component ◽  
Expression Data

TUMOR CLASSIFICATION BASED ON INDEPENDENT COMPONENT ANALYSIS

2006 ◽  
Vol 20 (02) ◽  
pp. 297-310 ◽  
Author(s):  
CHUN-HOU ZHENG ◽  
YAN CHEN ◽  
XIU-XIA LI ◽  
YI-XUE LI ◽  
YUN-PING ZHU
Keyword(s):  
Gene Expression ◽  
Independent Component Analysis ◽  
Dna Microarray ◽  
Gene Expression Data ◽  
Component Analysis ◽  
Tumor Classification ◽  
Independent Component ◽  
Support Vector ◽  
Expression Data ◽  
Microarray Gene Expression

This paper proposes a new method for tumor classification using gene expression data, which mainly contains three steps. Firstly, the original DNA microarray gene expression data are selected using t-statistics. Secondly, the selected genes are modeled by Independent Component Analysis (ICA). Finally, Support Vector Machine (SVM) is used to classify the modeling data. To show the validity of the proposed method, we apply it to classify two DNA microarray data sets involving various human normal and tumor tissue samples. The experimental results show that the method is efficient and feasible.

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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