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The high-resolution WDR data collected provide a valuable and unique dataset for improving semi-empirical WDR models and validating Computational Fluid Dynamic (CFD) models.
The aim of this is to gain greater knowledge of the types of elements which will also be of use in calibrating the numerical models and validating the simplified methods.
In order to fill these lacks, we propose a general approach, called Application-based DOmain Modeling (ADOM), which enables specifying domains and applications similarly, (re)using domain knowledge in application models, and validating the application models against the relevant domain models.
We cover all major steps of a typical study, including examining the data for potential biases, discovery of biomarkers, testing the robustness of the identified biomarkers, building predictive models and validating the results in independent data.
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We use the training data sets to build the models and validate the selected models using the validation data sets.
We developed the models and validated them in European patients to ensure generalisability.
By RT-PCR-seq, we analyzed the IGYP gene models and validated their expressions as active genes during infection.
Instead, we should first focus on searching for the best business model and validating our assumptions.
Advances in science can be made using computational modeling and validating the models with additional data.
Further work will focus on refining the model and validating the aforementioned conjectures.
The second example, cluster 68_S6: showsold_6: 215962–216777 shows 5 peptides clearly supporting the filtered model and validating one of the three introns in the gene models.
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