Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/30018
Title: Tumor classification based on non-negative matrix factorization using gene expression data
Authors: Zheng, CH
Ng, TY 
Zhang, L 
Shiu, CK 
Wang, HQ
Keywords: Gene expression data
Gene selection
Nonnegative matrix factorization
Tumor classification
Issue Date: 2011
Publisher: Institute of Electrical and Electronics Engineers
Source: IEEE transactions on nanobioscience, 2011, v. 10, no. 2, 5942177, p. 86-93 How to cite?
Journal: IEEE transactions on nanobioscience 
Abstract: This paper presents a new method for tumor classification using gene expression data. In the proposed method, we first select genes using nonnegative matrix factorization (NMF) or sparse NMF (SNMF), and then we extract features from the selected genes by virtue of NMF or SNMF. At last, we apply support vector machines (SVM) to classify the tumor samples using the extracted features. In order for a better classification, a modified SNMF algorithm is also proposed. The experimental results on benchmark three microarray data sets validate that the proposed method is efficient. Moreover, the biological meaning of the selected genes are also analyzed.
URI: http://hdl.handle.net/10397/30018
ISSN: 1536-1241
EISSN: 1558-2639
DOI: 10.1109/TNB.2011.2144998
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