Gene expression data classification based on non-negative matrix factorization

Chun-Hou Zheng; Ping Zhang; Lei Zhang; Xin-Xin Liu; Ju Han

doi:10.1109/IJCNN.2009.5178606

Neural Networks, IEEE - INNS - ENNS International Joint Conference on

Gene expression data classification based on non-negative matrix factorization

Year: 2009, Pages: 3542-3547

DOI Bookmark: 10.1109/IJCNN.2009.5178606

Authors

Chun-Hou Zheng, College of Information and Communication Technology, Qufu Normal University, Rizhao, Shandong, 276826 China
Ping Zhang, Institute of automation, Qufu Normal University, Rizhao, Shandong 276826 China
Lei Zhang, Biometric Research Center, Dept. of Computing, The Hong Kong Polytechnic University, China
Xin-Xin Liu, College of Information and Communication Technology, Qufu Normal University, Rizhao, Shandong, 276826 China
Ju Han, Department of Control Engineering, Academy of Armored Force Engineering, Beijing, 100072 China

Abstract

With the advent of DNA microarrays, it is now possible to use the microarrays data for tumor classification. Yet previous works have not use the nonnegative information of gene expression data. In this paper, we propose a new method for tumor classification using gene expression data. In this method, we first select genes using nonnegative matrix factorization (NMF) and sparse NMF (SNMF). Then we extract features of the selected gene data by virtue of NMF and SNMF. At last, support vector machines (SVM) was applied to classify the tumor samples based on the extracted features. To better fit for classification aim, a modified SNMF algorithm is also proposed. The experimental results on three microarray datasets show that the method is efficient and feasible.

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Gene expression data classification based on non-negative matrix factorization

Authors

Abstract

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