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 ...

• Type: Journal
• Year: 2010
• Where: BMCBI
• Authors: Chi Kin Chow, Hai Long Zhu, Jessica Lacy, Winston Patrick Kuo
• Keywords: BMCBI 2010, Feature Gene, Gene, Gene Extraction