Validation is determined by a comparison to the significance of the interaction term using linear regression fit.
The source(s) of evidence sought for any particular validation is determined by the desired interpretation(s) [ 34, 35].
Our statistical validation is determined by adopting a cluster-centric annotation procedure that allows transfer of different types of annotation, including structure and function.
The process of validation is determined by a variety of factors including: a) the "comfort level" of the individual agencies or organizations that evaluate the test, which is a function of their statutory and regulatory mandate (Zeigler and Stokes 1998); b) influence from pressure groups (Garthoff 2005); and c) the perceived need for a new test protocol.
The expression of 15 genes APLNR, CEBPA, CNTN1, ELF5, EPHA1, FBLN1, FOSB, FST, HOXD10, KIF14, LMO4, LPAR2, NME3, NR2F1, RASSF7, RHOBTB3, RPRM, TFF3 selected for validation was determined using Taqman quantitative RT-PCR (qRT-PCR) gene expression Assay On Demand Probe/Primers (Applied Biosystems, Foster City, CA), with housekeeping gene GAPDH as an endogenous control.
The expression level of 18 genes APLNR, CEBPA, CNTN1, ELF5, EPHA1, FBLN1, FOSB, FST, HOXD10, KIF14, LMO4, LPAR2, NME3, NR2F1, RASSF7, RHOBTB3, RPRM, TFF3 selected for validation was determined using Taqman qRT-PCR gene expression Assay On Demand Probe/Primers (Applied Biosystems, Foster City, CA), with housekeeping gene GAPDH as an endogenous control.
The precision of SPE within single-laboratory validation was determined at two levels, i.e., by (a) intra-day assay which was based on five extraction replicates within 1 day and (b) an inter-day approach based on three extractions daily performed over five consecutive days, respectively.
Moreover, these modules should update corresponding to the updatings of local LTI models, which validations are determined by the model approximation errors and the optimal index of local design.
The optimal model parameters γ and σ that provide the smallest error in inner cross-validation are determined by performing a grid search over the range of 1 to 10000 and 0.01 to 10, respectively.
At the end of the cross validation, the estimate is determined as the mean of the features of the k-fold model.
