Because bisulfite pyrosequencing cannot distinguish between single CpG and allele methylation errors, validation of epimutations requires the analysis of individual DNA molecules.
Although it is possible to use base quality values to discern true allelic variations from sequencing errors, validation is a key step for detection of true SNPs [ 15].
We study the designing aspect of DNNs from model-specific and data-specific perspectives with focus on three performance metrics: training time, training error and, validation error.
In this study, through shape error metric (" Validation metrics" section), cMEM reconstructed time courses were evaluated for the first time.
In addition, we evaluated the performance of each classifier on a seventh independent dataset (Vijver et al. [ 3]). Figure 6A and 6B show the DLCV error and validation error as a function of the number of datasets pooled.
We explored how the model training errors and model validation errors (predictive power) vary in relation to different settings of the omega interval.
Consequently, we should treat motif enrichment analysis as one error-prone validation and use it together with other validations (co-citations, functional category or pathway enrichments).
Here criterion 2 is tested according to the validation error, and validation error has been achieved in Step 6.
This is a common error in model validation, because this circular validation process doesn't give you any information about the accuracy of the algorithm.
Results show that all three methods produce NNs capable of zero validation error using a nearest integer error criterion.
The accuracy and quality of the PLS models were evaluated by using the root mean square error of cross validation (RMSECV), the standard error of cross validation (SECV), R, the number of factors, and the ratio performance deviation (RPD).
