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ArrayMining: a modular web-application for microarray analysis combining ensemble and consensus methods with cross-study normalization

Glaab, Enrico; Garibaldi, Jon; Krasnogor, Natalio


Enrico Glaab

Jon Garibaldi

Natalio Krasnogor


Statistical analysis of DNA microarray data provides a valuable diagnostic tool for the investigation of genetic components of diseases. To take advantage of the multitude of available data sets and analysis methods, it is desirable to combine both different algorithms and data from different studies. Applying ensemble learning, consensus clustering and cross-study normalization methods for this purpose in an almost fully automated process and linking different analysis modules together under a single interface would simplify many microarray analysis tasks.

We present, a web-application for microarray analysis that provides easy access to a wide choice of feature selection, clustering, prediction, gene set analysis and cross-study normalization methods. In contrast to other microarray-related web-tools, multiple algorithms and data sets for an analysis task can be combined using ensemble feature selection, ensemble prediction, consensus clustering and cross-platform data integration. By interlinking different analysis tools in a modular fashion, new exploratory routes become available, e.g. ensemble sample classification using features obtained from a gene set analysis and data from multiple studies. The analysis is further simplified by automatic parameter selection mechanisms and linkage to web tools and databases for functional annotation and literature mining.

Conclusion: is a free web-application for microarray analysis combining a broad choice of algorithms based on ensemble and consensus methods, using automatic parameter selection and integration with annotation databases.


Glaab, E., Garibaldi, J., & Krasnogor, N. (2009). ArrayMining: a modular web-application for microarray analysis combining ensemble and consensus methods with cross-study normalization. BMC Bioinformatics, 10, Article Article 358.

Journal Article Type Article
Publication Date Jan 1, 2009
Deposit Date Apr 21, 2010
Publicly Available Date Apr 21, 2010
Journal BMC Bioinformatics
Electronic ISSN 1471-2105
Publisher Springer Verlag
Peer Reviewed Peer Reviewed
Volume 10
Article Number Article 358
Keywords microarray, gene expression, feature selection, prediction, classification, machine learning, clustering, network analysis, co-expression, pathway, normalization, cross-study, annotation, visualization
Public URL
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