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When analyzing the relationship between genes under different scenarios, the integration of different microarray experiments becomes a relevant task. This paper presents a framework to address some intrinsic problems of integration, due for instance to scaling issues, error bias, different experimental conditions or technology and protocols. Our approach projects original microarray data in a common transformed space to create a common representation of different microarray datasets. This approach allows us to integrate data from various microarray platforms or microarrays based on different experimental conditions. We validate our framework with experiments on real microarray datasets. The results suggest that our approach can be a profitably exploited for microarray data integration and further gene expression analysis applications.