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Within each class of RNA-models, the best model is evaluated by an Akaike Information criterion (AICc) test.
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When a constrained region causes a multicollinearity problem, contributions of each variable in the best model are evaluated by the approximate model derived from substituting correlations between explanatory variables into the best model.
The globularity of the best model was evaluated according to our recent work [27].
The rise times for the best models were evaluated as the average of 20 repeats.
In order to evaluate the performance of our models on independent data, we first trained our models on 80% of data by ten-fold cross validation and later the performance of the best models were evaluated on remaining 20% independent data.
For ML the best substitution model was evaluated using Modeltest 3.7 [ 69] that determined SYM + G as the best-fit model, according to Akaike's information criterion (AIC).
The accuracy and OR of the best candidate model was evaluated.
For Bayesian analysis the best substitution model was evaluated through MrModelTest v2.2 [ 70], a modified version of David Posada's Modeltest 3.6 rewritten to compare all of the 24 models that can be implemented in MrBayes version 3, which also selected SYM+G as the best-fit model (AIC).
The best-fitting model was evaluated using the correlation coefficient.
The best fitting nucleotide substitution model was evaluated using a web-based tool, FindModel [83].
The model with the minimum DOPE score was selected as the best model and the stereochemical quality of the model was evaluated and confirmed with PROCHECK.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com