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Issue No. 05 - Sept.-Oct. (2013 vol. 10)
ISSN: 1545-5963
pp: 1
Ting Chen , Dept. of Electr. & Comput. Eng., Texas A&M Univ., College Station, TX, USA
Ulisses M. Braga-Neto , Dept. of Electr. & Comput. Eng., Texas A&M Univ., College Station, TX, USA
A statistic tool for the detection of multivariate Boolean relationships is presented, with applications in the inference of gene regulatory mechanisms. A statistical test is developed for the detection of a nonzero discrete Coefficient of Determination (CoD) between predictor and target variables. This is done by framing the problem in the context of a stochastic logic model that naturally allows the inclusion of prior knowledge if available. The rejection region, p-value, statistical power, and confidence interval are derived and analyzed. Furthermore, the issue of multiplicity of tests due to presence of numerous candidate genes and logic relationships is addressed via FWER- and FDR-controlling approaches. The methodology is demonstrated by experiments using synthetic data and real data from a study on ionizing radiation (IR) responsive genes. The results indicate that the proposed methodology is a promising tool for detection of gene regulatory relationships from gene-expression data. Software that implements the COD test is available online as an R package.
Statistical analysis, Bioinformatics, Stochastic processes, Boolean functions, Logic functions

Ting Chen and U. M. Braga-Neto, "Statistical Detection of Boolean Regulatory Relationships," in IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol. 10, no. 5, pp. 1, 2014.
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