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We also compute the a posteriori most probable regulator model for each gene, which we refer to as the maximum a posteriori (MAP) model.
Just because a gene appears in a regulator's list of predicted targets, does not mean that regulator is the most probable regulator for that target.
There are however two different criteria that could be used to denote a gene as the most probable regulator of a target gene.
Nevertheless, the most probable regulator combination results in Figure 9 show very high accuracy for our MAP method, which is very clearly superior to all other methods, except regression when using the full list of genes.
In this analysis we provide results for both criteria for selecting the most probable regulator, the lowest adjusted p-value from the meta-analysis and the greatest number of data sets with significant correlations.
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IFNG, one of the most probable upstream regulators, is known for its paramount role in acute rejection.
For each of the 827 regulator-module pairs we reconstructed the most probable (primary) regulation pathway that starts with the regulator and ends with a TF that binds some of the target genes.
The modules, regulators and paths are inferred simultaneously, resulting in the most probable physical model of gene regulation that underlies the observed data.
Figure 7 shows results of the ChIP evaluation based on the MAP regulator configuration for every gene, ranked by the posterior probability of this most probable model.
We report the probability of the most probable path for each of the equally probable gene segment sets.
Of these, a deletion of 1,618 base pairs in an ORF encoding a TetR-family transcriptional regulator (GBAA0834) appears to be the most probable candidate for high-level ciprofloxacin resistance.
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