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Open Access Software

FiGS: a filter-based gene selection workbench for microarray data

Taeho Hwang1, Choong-Hyun Sun2, Taegyun Yun1 and Gwan-Su Yi12*

Author Affiliations

1 Department of Bio and Brain Engineering, KAIST, Daejeon 305-701, South Korea

2 Department of Computer Science, KAIST, Daejeon 305-701, South Korea

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BMC Bioinformatics 2010, 11:50  doi:10.1186/1471-2105-11-50

Published: 26 January 2010

Abstract

Background

The selection of genes that discriminate disease classes from microarray data is widely used for the identification of diagnostic biomarkers. Although various gene selection methods are currently available and some of them have shown excellent performance, no single method can retain the best performance for all types of microarray datasets. It is desirable to use a comparative approach to find the best gene selection result after rigorous test of different methodological strategies for a given microarray dataset.

Results

FiGS is a web-based workbench that automatically compares various gene selection procedures and provides the optimal gene selection result for an input microarray dataset. FiGS builds up diverse gene selection procedures by aligning different feature selection techniques and classifiers. In addition to the highly reputed techniques, FiGS diversifies the gene selection procedures by incorporating gene clustering options in the feature selection step and different data pre-processing options in classifier training step. All candidate gene selection procedures are evaluated by the .632+ bootstrap errors and listed with their classification accuracies and selected gene sets. FiGS runs on parallelized computing nodes that capacitate heavy computations. FiGS is freely accessible at http://gexp.kaist.ac.kr/figs webcite.

Conclusion

FiGS is an web-based application that automates an extensive search for the optimized gene selection analysis for a microarray dataset in a parallel computing environment. FiGS will provide both an efficient and comprehensive means of acquiring optimal gene sets that discriminate disease states from microarray datasets.