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Short Communication Open Access
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.
Here, we provided a briefly overview of current meta-analytic approaches together with the basic critical issues in performing meta-analysis of genomic data, with the aim of helping researchers to evaluate the quality of existing, published data and obtain more detailed information on what will be the best strategy to adopt to execute a good meta-analysis.
Bioinformatics, Biomedical data mining, Computational Biology, Genome Data Mining