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| Enrico Glaab; Jonathan M. Garibaldi; Natalio Krasnogor. |
| We present two novel web-applications for microarray and gene/protein set analysis, ArrayMining.net and TopoGSA. These bioinformatics tools use integrative analysis methods, including ensemble and consensus machine learning techniques, as well as modular combinations of different analysis types, to extract new biological insights from experimental transcriptomics and proteomics data. They enable researchers to combine related algorithms and datasets to increase the robustness and accuracy of statistical analyses and exploit synergies of different computational methods, ranging from statistical learning to optimization and topological network analysis. |
| Tipo: Presentation |
Palavras-chave: Cancer; Genetics & Genomics; Bioinformatics. |
| Ano: 2011 |
URL: http://precedings.nature.com/documents/5598/version/1 |
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| Enrico Glaab; Jonathan M. Garibaldi; Natalio Krasnogor. |
| DNA microarray experiments provide a means to understand cancer and genetic diseases on a molecular level, improve diagnosis and identify new drug targets. However, choosing appropriate data processing methods and parameters is a difficult and time-consuming task, particularly for researchers without prior experience in this field. 
We present *ArrayMining.net*, a free web-service for automatic microarray analysis to address these issues. ArrayMining.net covers several major areas in statistical microarray analysis - Feature Selection, Clustering, Prediction, Gene Set and Network Analysis - providing access to several algorithms for each of these tasks based on a single, easy-to-use interface. |
| Tipo: Poster |
Palavras-chave: Cancer; Genetics & Genomics; Bioinformatics. |
| Ano: 2011 |
URL: http://precedings.nature.com/documents/5552/version/1 |
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