As a consequence, these problems impact the sample representation and degrade the classification performance.
Although GSEA exploits pathway-based information to detect relevant genes, this method emphasizes a gene-centric approach to sample representation and classification.
The analysis has, therefore, neutralized the potential impacts produced by differences in type of studies, sample size, ethnicity, statistical procedures, sample representation and other factors and provided robust and consistent findings often lacking in genetic association studies.
Increased accuracy in sample representation, and allele frequency estimation may have been achieved by performing separate PCR reactions in each individual and pooling of PCR products [22], although this would have resulted in increased costs and DNA use.
An expected ploidy of 20 was used (i.e., allele frequencies calculated in increments of 5%) in order to best balance accurate sample representation and computational efficiency.
Therefore, those factors may have some bearing toward the validity of the sample representations.
There was unequal and yet small sample size representations and the nature of the data obtained [ 29].
Owing to the existence of variability in sample representation, distribution selection, and distribution parameter estimation, flood quantile estimation is subjected to various levels of uncertainty, which is not negligible and avoidable.
Conflicting evidence may have arisen from a large number of factors such as the type of association studies (e.g., familial versus population studies), sample size, ethnic variation, formulation of statistical tests, sample representation, behavior measurement and statistical controls included.
A sample representation of an aggressive and smooth data can be seen in Fig. 9. Fig. 9 Examples of histogram comparison of aggressive and smooth driving sessions for different features.
