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

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.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.DNA microarray experiments provide a powerful means to improve our understanding of diseases with a genetic basis or contribution. Commercial microarray chips for highly accurate diagnosis of several cancers are already available on the market [1,2] and pharmaceutical companies are using DNA-chip technology to identify new drug targets.The fast accumulation of gene expression data in public online databases and the great variety of available analysis methods, however, also pose new challenges. Integrating data from different sources, choosing appropriate normalization, analysis and cross-validation methods and selecting suitable parameters requires substantial time and effort. Since different algorithms have different strengths and similar data from independent studies is often available, it is desirable to combine multiple methods and/or data sets to obtain more robust and accurate results. This creates ample opportunities