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We illustrate the inference technique to obtain the value of the parameter p from microarray experiments with a specific example.
In order to clearly illustrate the inference performance of C3NET in dependence on the different network types, we plot in Figure 5 a summary of the corresponding F-scores.
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This paper illustrates the inferences of the second phase of the investigation that is carried out during the rainy season of the year.
He then presents a number of examples that illustrate the limits of inference without theory: the effect of unemployment benefits on unemployment duration; the effect of public welfare on women's labor market and demographic outcomes; the effect of school attainment on earnings; and a famous field experiment in education dealing with class size.
Figure 1A illustrates the basic inference principle.
Having successfully assessed the validity and robustness of this approach, we can then infer domain-disease in an ab initio way and illustrate the strong agreement of the inference results with evidence of genome-wide association studies for four common human diseases, including type 1 diabetes, type 2 diabetes, Crohn's disease, and breast cancer.
In order to clearly illustrate the construction of BIN, the inference nets for healthy condition have not been presented in Figure 3.
Figures 10(a)– 10(d) illustrate the accuracy of phylogenetic inference by the same methods in sets of 50, 100, 200, and 300 random species, respectively.
Once having assessed the validity of this approach, we show the possibility of ab initio inference of domain-disease associations and gene-disease associations, and we illustrate the strong agreement between our inferences and the evidences from genome-wide association studies for four common diseases (type 1 diabetes, type 2 diabetes, Crohn's disease, and breast cancer).
Extensive simulations and real biomedical data examples illustrate the applicability of the proposed inference methods for both nested and non-nested models.
The results illustrate the capability of the proposed inference technique to generate BNs that are similar to the original BNs by using few state transitions when the connectivity is known.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com