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Abstract:
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Sign inconsistency is a common dilemma faced in routine data analysis of large genomics data obtained from cell lines and patients. Genes showing positive association for one dataset may turn into negative in another. Inconsistency is commonly treated as a source of random errors and thereby ignored at the next round of analysis. However, sign inconsistency may be an indication of hidden factors that are undetected due to lack of adequate analysis. In this talk, we will present an approach to deal with sign inconsistency when utilizing multiple sources of gene expression profiling, genetic markers, and complex disease phenotypes.
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