The rapid advances in high-throughput technologies, such as microarrays have revolutionizing the knowledge and understanding of biological systems and genetic signatures of human diseases. This has led to the generation and accumulation of a large amount of genomic data that need to be adequately integrated to obtain more reliable and valid results than those from individual experiments. Meta-analysis of microarray data is one of the most common statistical techniques used for combining multiple data sets. Despite its remarkable successes in discovering molecular subtypes, underlying pathways and biomarkers for the pathological process of interest, this method possesses several limitations.
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