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ABSTRACT: Ambitious efforts to characterize disease have been made worldwide, mainly in cancer, with initiatives such as the Cancer Genome Atlas. Many of these cost-intensive studies use cutting-edge technologies to delve deeply into the intrinsic genomic, transcriptomic, proteomic, metabolomic, etc, (ie, omics) type of data to better explain the phenotype. But while more data is being stored, the complexity of cancer seems to challenge even more our ability to understand its nature and thus to uncover useful bio-physiological information. We strongly believe that data analytics, as well as our understanding of ‘normal’ cases, are still in their infancy, opening great opportunities in translational cancer research to pursue precision medicine through Big Omics Data analytics. 

Keywords: Data Interpretation, Statistical, Genomics, Microarray Analysis, Proteomics, Statistics as Topic