Statistical Distribution as a Way for Lower Gene Expressions Threshold Cutoff
AbstractWhile in mathematics (and in logic) the basic divide is between ‘true’ and ‘false’, in experimental science the frontier is between ‘relevant’ and ‘irrelevant’ and this is a much more tricky border. The classical way to track this frontier builds upon inferential statistics (signal analysis is a synonymous more popular among engineers) and is based on the definition of what we intend for ‘randomness’ in a given situation. Here we comment on the setting of the threshold between ‘informative’ and ‘random’ territories in the case of gene expression data where the definition of randomness is not only a ‘statistical’ but a ‘biological’ affair.
How to Cite
Tien, B. T., Giuliani, A., & Selvarajoo, K. (2018). Statistical Distribution as a Way for Lower Gene Expressions Threshold Cutoff. Organisms. Journal of Biological Sciences, 2(2), 55–58. https://doi.org/10.13133/2532-5876_4.6
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