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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.
To identify the most probable model, we computed the posterior probability of the three scenarios above using a logistic regression approach on the first 1% simulations (Fagundes et al. 2007; Beaumont 2008).
They can be estimated by (see [ 19]) (6) Once the probability of the model is known, we can select the most probable model from a set of competitive models using the Bayes factor BF = p(Y | M i )/ p(Y | M j ).
The most probable model is chosen according to a decision algorithm.
The selection of the most probable model from among a finite set of fracture geometry and loading model is carried out using Bayes factors.
A Bayesian approach is presented for selecting the most probable model class among a set of damage mechanics models for fatigue damage progression in composites.
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Then the MHT algorithm we develop sorts through all the data and how data relate to each other, and it develops the most probable models for what's happening in these different areas of interest.
Each docking returned the 100 most probable models from unbound monomer components.
The energetically most probable models, according to the Rosetta energy function, were then identified, reducing the set to 200000 models per domain.
Thus, among all the six different models we considered, model M5 and M6 are the most probable models given the data, highlighting the importance of the maximal transcription rate.
The aims of this study were: (1) to predict the binding sites that link the alpha and beta1 subunits, using computational modeling and molecular docking; (2) to evaluate helix-helix interactions in pairs of helices; and (3) to generate most-probable models for the alpha and beta1 subunits, in both the monomeric and the assembled form.
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