Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/6253
Title: Error margin analysis for feature gene extraction
Authors: Chow, CK
Zhu, HL
Lacy, J
Kuo, WP
Keywords: Algorithms
Gene expression profiling
Oligonucleotide array sequence analysis
Pattern recognition
Automated
Issue Date: 11-May-2010
Publisher: BioMed Central
Source: BMC bioinformatics, 11 May 2010, v. 11, 241, p. 1-17 How to cite?
Journal: BMC bioinformatics 
Abstract: Background: Feature gene extraction is a fundamental issue in microarray-based biomarker discovery. It is normally treated as an optimization problem of finding the best predictive feature genes that can effectively and stably discriminate distinct types of disease conditions, e.g. tumors and normals. Since gene microarray data normally involves thousands of genes at, tens or hundreds of samples, the gene extraction process may fall into local optimums if the gene set is optimized according to the maximization of classification accuracy of the classifier built from it.
Results: In this paper, we propose a novel gene extraction method of error margin analysis to optimize the feature genes. The proposed algorithm has been tested upon one synthetic dataset and two real microarray datasets. Meanwhile, it has been compared with five existing gene extraction algorithms on each dataset. On the synthetic dataset, the results show that the feature set extracted by our algorithm is the closest to the actual gene set. For the two real datasets, our algorithm is superior in terms of balancing the size and the validation accuracy of the resultant gene set when comparing to other algorithms.
Conclusion: Because of its distinct features, error margin analysis method can stably extract the relevant feature genes from microarray data for high-performance classification.
URI: http://hdl.handle.net/10397/6253
EISSN: 1471-2105
DOI: 10.1186/1471-2105-11-241
Rights: © 2010 Chow et al; licensee BioMed Central Ltd.
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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