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Beyond Bioinformatics | |
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Title
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Effective feature selection framework for cluster analysis of microarray data | |||||||||||||||||||||||||||
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Authors
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Gouchol Pok1, Jyh-Charn Steve Liu2, Keun Ho Ryu3* | ||||||||||||||||||||||||||||
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Affiliation
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1Yanbian University of science and Technology, Dept. of Computer Science, Yanji, Jilin, China 133000; 2Texas A&M University, Dept. of Computer Science, College Station, TX, USA; 3Chungbuk National University, DB Bioinformatics Lab, Cheongju, Chungbuk, Korea.
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khryu@dblab.chungbuk.ac.kr | ||||||||||||||||||||||||||||
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Article Type
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Hypothesis | ||||||||||||||||||||||||||||
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Date
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Received May 25, 2009; Revised February 18, 2010; Accepted February 24, 2010; Published February 28, 2010 | ||||||||||||||||||||||||||||
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Abstract |
The microarray technique has become a standard means in simultaneously examining expression of all genes measured in different circumstances. As microarray data are typically characterized by high dimensional features with a small number of samples, feature selection needs to be incorporated to identify a subset of genes that are meaningful for biological interpretation and accountable for the sample variation. In this article, we present a simple, yet effective feature selection framework suitable for two-dimensional microarray data. Our correlation-based, nonparametric approach allows compact representation of class-specific properties with a small number of genes. We evaluated our method using publicly available experimental data and obtained favorable results.
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Keywords |
gene expression microarray, feature selection, classification, clustering
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Citation
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Pok et al., Bioinformation 4(8): 385-389 (2010)
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Edited by
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P. Kangueane
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ISSN
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0973-2063
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Publisher
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Biomedical Informatics | ||||||||||||||||||||||||||||
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Copyright
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Publisher
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Copyright Transfer Agreement
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The authors of published articles in Bioinformation automatically transfer the copyright to the publisher upon formal acceptance. However, the authors reserve right to use the information contained in the article for non commercial purposes.
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License
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This is an open-access article, which permits unrestricted use, distribution, and reproduction in any medium, for non-commercial purposes, provided the original author and source are credited.
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