Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/12237
Title: Gene expression data classification using locally linear discriminant embedding
Authors: Li, B
Zheng, CH
Huang, DS
Zhang, L 
Han, K
Keywords: Classification
Gene expression data
Locally linear discriminant embedding.
Manifold learning
Issue Date: 2010
Publisher: Pergamon Press
Source: Computers in biology and medicine, 2010, v. 40, no. 10, p. 802-810 How to cite?
Journal: Computers in biology and medicine 
Abstract: Gene expression data collected from DNA microarray are characterized by a large amount of variables (genes), but with only a small amount of observations (experiments). In this paper, manifold learning method is proposed to map the gene expression data to a low dimensional space, and then explore the intrinsic structure of the features so as to classify the microarray data more accurately. The proposed algorithm can project the gene expression data into a subspace with high intra-class compactness and inter-class separability. Experimental results on six DNA microarray datasets demonstrated that our method is efficient for discriminant feature extraction and gene expression data classification. This work is a meaningful attempt to analyze microarray data using manifold learning method; there should be much room for the application of manifold learning to bioinformatics due to its performance.
URI: http://hdl.handle.net/10397/12237
ISSN: 0010-4825
EISSN: 1879-0534
DOI: 10.1016/j.compbiomed.2010.08.003
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