Gene expression data classification based on non-negative matrix factorization

Authors:
Chun-Hou Zheng;Ping Zhang;Lei Zhang;Xin-Xin Liu;Ju Han
Affiliations:
College of Information and Communication Technology, Qufu Normal University, Rizhao, Shandong and Intelligent Computing Lab, Institute of Intelligent Machines, Chinese Academy of Sciences, Hefei, ...;Institute of automation, Qufu Normal University, Rizhao, Shandong, China;Biometric Research Center, Dept. of Computing, The Hong Kong Polytechnic University, Hong Kong, China;College of Information and Communication Technology, Qufu Normal University, Rizhao, Shandong, China;Department of Control Engineering, Academy of Armored Force Engineering, Beijing, China
Venue:
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Year:
2009

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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.