Background: Genomic technologies have allowed a large-scale molecular characterization of living organisms, involving the generation and interpretation of data at an unprecedented scale. Advanced platforms for the detection of different types of genomic alterations have been developed and applied to analyses of living organisms and, in particular, cancer genomes. It is clear now that studies based on a single platform are limited compared with the extent of knowledge gain possible when exploiting different platforms together. There is therefore a need for systematic methodologies facilitating data management, visualization, and integration.Materials and Methods: We present a 3-state model (3-MDI) that integrates several technological platforms, visualizing and prioritizing different biological scenarios, and thus enables researchers to pursue data exploration in an educated way, where some or all of the explored avenues could be used to determine thresholds for differential changes in the examined platforms, or may help identify genes that follow an interesting pattern.Conclusion: Each additional genomic data dimension increases both the amount of information and consequently the biological and computational complexity of the analysis. We have demonstrated here, however, that multidimensional genomic data driven approaches can facilitate finding relevant genes that would otherwise largely remain unexplored because they would be overlooked in traditional analyses of individual biological experiments.
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