A statistical problem for inference to regulatory structure from associations of gene expression measurements with microarrays
Author(s) -
Tianjiao Chu,
Clark Glymour,
Richard Scheines,
Peter Spirtes
Publication year - 2003
Publication title -
bioinformatics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.599
H-Index - 390
eISSN - 1367-4811
pISSN - 1367-4803
DOI - 10.1093/bioinformatics/btg011
Subject(s) - conditional independence , inference , bayes' theorem , statistic , computer science , statistical inference , statistical hypothesis testing , computational biology , simulated annealing , artificial intelligence , bayesian probability , data mining , biology , mathematics , statistics , machine learning
One approach to inferring genetic regulatory structure from microarray measurements of mRNA transcript hybridization is to estimate the associations of gene expression levels measured in repeated samples. The associations may be estimated by correlation coefficients or by conditional frequencies (for discretized measurements) or by some other statistic. Although these procedures have been successfully applied to other areas, their validity when applied to microarray measurements has yet to be tested.
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