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For Bayesians, the most important probabilistic rule of inference is given by a principle of conditionalization.

Following this, a detailed comparison between the classical Bayesian inference and the MrE inference is given.

For each alternative haplotype inference the first corresponding genotype inference is given.

An overview of classifiers that are used in CDSS is introduced in Section 2. The experiment to be conducted is described in details in Section 3. The second phase of rt-CDSS namely the decision inference is given in Section 4. Section 5 concludes the paper.

The pseudo-code of this inference is given in Figure 2. Briefly, the inference algorithm takes the thresholds and list of cases and controls as an input, calculates the number of cases and controls sharing each SH, and evaluates whether a SH has the features of an interesting SH taking into account both the cases and controls.

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Finally, concluding remarks and further directions for miRNA-mediated and GRNs inference are given.

The sizes of multipartite circles for the other five mitotypes except ole predicted by this inference are given in Additional file 1, Table S1.

A comparison of the qPCR results with our genotype inferences is given in Table 1.

A graphical representation of all these phylogenetic inferences is given in Figure 2. Four of the six s100 genes found in the shark genome also belong to this group of genes common to teleosts and mammals (the remaining two exhibit no unambiguous orthologs).

Policy inferences are given towards a more equal distribution of WF.

Detailed information on sample collection, marker selection, PCR amplification, and phylogenetic inferences are given in the Text S2, S3, S4, S5 and S6 and Fig. S1.

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