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To be able to directly compare the prediction accuracies accomplished using RF versus ANN, we implemented the cross validation procedure described by Ross et al [ 14] and divided the samples into a training set (n = 79) and a test set (n = 25, see Materials and Methods for details and Table 1).
We will check the validity of our 3 existing quality criteria and continue to work out and implement internal and external validation procedures.
Testing and validation procedures for the implemented control strategies have been performed using an implementation of the aforementioned dynamic model in MATLAB-Simulink, assuming different trajectory curvatures and vehicle speeds.
Validation procedures were applied.
There are also stringent accreditation and validation procedures in place for language schools in other countries.
Several study designs exist for validation procedures.
The validation procedures and shRNA constructs chosen for assays are described in Supplementary Materials and Supplementary Tables S3 S5.
Hence, we conducted a series of scale validation procedures.
Due to the widespread support of XML and the simple data formats we propose, this automated validation procedure can be implemented in a variety of programming languages.
The validation procedure presented here was implemented to provide a standardized method to assess DNA-microarray data quality generated in our laboratory and should be well-suited for use in other laboratories.
Downstream of these products, this paper provides some insights of the wide validation procedure that is going to be implemented to check and to improve the quality of the results.
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