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

Glaab, Enrico and Garibaldi, Jon and Krasnogor, Natalio (2009) ArrayMining: a modular web-application for microarray analysis combining ensemble and consensus methods with cross-study normalization. BMC Bioinformatics, 10 . Article 358. ISSN 1471-2105

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Official URL: http://www.biomedcentral.com/1471-2105/10/358

Abstract

Background: 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. Results: We present ArrayMining.net, 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: ArrayMining.net 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.

Item Type:Article
Uncontrolled Keywords:microarray, gene expression, feature selection, prediction, classification, machine learning, clustering, network analysis, co-expression, pathway, normalization, cross-study, annotation, visualization
Schools/Departments:Faculty of Science > School of Computer Science
ID Code:1271
Deposited By:Glaab, E
Deposited On:21 Apr 2010 16:00
Last Modified:21 Apr 2010 16:00

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