Abstract
BackgroundThe 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.ResultsFiGS 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.ConclusionFiGS 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.
Highlights
The selection of genes that discriminate disease classes from microarray data is widely used for the identification of diagnostic biomarkers
Prophet enables a comparison of the performance of different feature selection methods and classifiers with leave-oneout cross-validation (LOOCV) errors [1]
We tested the performance of all the possible gene selection procedures that can be generated by FiGS to six binary microarray datasets
Summary
The selection of genes that discriminate disease classes from microarray data is widely used for the identification of diagnostic biomarkers. 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. Gene selection methods for microarray data analysis are important to identify the significant genes that distinguish disease classes and to use these selected genes as diagnostic markers in clinical decisions. Each method demonstrated a proper level of quality to predict disease states in its own test datasets, but the performance level was only partially validated in the sense that a limited number of sample datasets, gene selection algorithms, and the parameters in the test were used. There have been several tools that support a comparative analysis of different gene selection methods. Gene Expression Model Selector (GEMS) provides an automatic selection of several feature selection methods and different types of multi-category support vector machine (MC-SVM) algorithms [2]. The aforementioned tools are useful but each has room for improvement in terms of automation, the diversity of method comparisons, or the information content in the output report
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