Error margin analysis for feature gene extraction.
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.


CONCLUSIONS
Because of its distinct features, error margin analysis method can stably extract the relevant feature genes from microarray data for high-performance classification.
Chow
Chi Kin
CK
Research institute of Innovative Products and Technologies, The Hong Kong Polytechnic University, Hong Kong SAR, PR China.
Zhu
Hai Long
HL
Lacy
Jessica
J
Kuo
Winston P
WP
D000465:Algorithms; D020869:Gene Expression Profiling / Q000379:methods*; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods; D010363:Pattern Recognition, Automated / Q000379:methods
Talanta. 2007 Mar 15;71(4):1679-83
19071508
BMC Bioinformatics. 2003 Nov 06;4:54
14604446
Proc Natl Acad Sci U S A. 2002 May 14;99(10):6562-6
11983868
Bioinformatics. 2006 Oct 1;22(19):2348-55
16844704
Mol Genet Metab. 2001 Jul;73(3):239-47
11461191
Cancer Res. 2005 Sep 15;65(18):8142-50
16166288
Bioinformatics. 2003 Nov 22;19(17):2246-53
14630653
Bioinformatics. 2001 Dec;17(12):1131-42
11751221
IEEE Trans Pattern Anal Mach Intell. 2004 Nov;26(11):1424-37
15521491