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Open Access Highly Accessed Software

geneCommittee: a web-based tool for extensively testing the discriminatory power of biologically relevant gene sets in microarray data classification

Miguel Reboiro-Jato1, Joel P Arrais3, José Luis Oliveira2 and Florentino Fdez-Riverola1*

Author Affiliations

1 Escuela Superior de Ingeniería Informática, Universidade de Vigo, Campus Universitario As Lagoas s/n, 32004 Ourense, Spain

2 DETI/IEETA, University of Aveiro, 3810-193 Aveiro, Portugal

3 Department of Informatics Engineering (DEI), Centre for Informatics and Systems of the University of Coimbra (CISUC), University of Coimbra, Coimbra, Portugal

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BMC Bioinformatics 2014, 15:31  doi:10.1186/1471-2105-15-31

Published: 30 January 2014

Abstract

Background

The diagnosis and prognosis of several diseases can be shortened through the use of different large-scale genome experiments. In this context, microarrays can generate expression data for a huge set of genes. However, to obtain solid statistical evidence from the resulting data, it is necessary to train and to validate many classification techniques in order to find the best discriminative method. This is a time-consuming process that normally depends on intricate statistical tools.

Results

geneCommittee is a web-based interactive tool for routinely evaluating the discriminative classification power of custom hypothesis in the form of biologically relevant gene sets. While the user can work with different gene set collections and several microarray data files to configure specific classification experiments, the tool is able to run several tests in parallel. Provided with a straightforward and intuitive interface, geneCommittee is able to render valuable information for diagnostic analyses and clinical management decisions based on systematically evaluating custom hypothesis over different data sets using complementary classifiers, a key aspect in clinical research.

Conclusions

geneCommittee allows the enrichment of microarrays raw data with gene functional annotations, producing integrated datasets that simplify the construction of better discriminative hypothesis, and allows the creation of a set of complementary classifiers. The trained committees can then be used for clinical research and diagnosis. Full documentation including common use cases and guided analysis workflows is freely available at http://sing.ei.uvigo.es/GC/ webcite.