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

BMC Bioinformatics
Taeho HwangGwan-Su Yi

Abstract

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. 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 ...Continue Reading

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Citations

May 13, 2010·Nucleic Acids Research·Choong-Hyun SunGwan-Su Yi
Oct 28, 2011·BMC Bioinformatics·Vid PodpečanKristina Gruden
May 16, 2012·BMC Systems Biology·Zhengzhang ChenNagiza F Samatova
Apr 29, 2015·Computers in Biology and Medicine·Aiguo WangGil Alterovitz
Nov 6, 2012·Journal of Biomedical Informatics·Xin SunJiawei Han
Oct 3, 2014·EURASIP Journal on Bioinformatics & Systems Biology·Thang VuEdward R Dougherty

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Software Mentioned

randomForest
e1071
FiGS
CBETH
Prophet
- package
Gene Expression Model Selector ( GEMS )
varSelRF
package
R

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