Sentence examples for identification of variance from inspiring English sources

MANOVA (identification of variance factors) was followed by ANOVA and LSD tests (within-factor differences between levels).

Interactions give rise to genotype dependent variance, and therefore the identification of variance quantitative trait loci can be an intermediate step to discover both epistasis and gene by environment effects (GxE).

Based on Monte Carlo validations, the quality of identification, accuracy of variance estimations and sensor noise robustness are discussed.

Most importantly, the search for gene signatures should be conducted by conditioning on the clinicopathologic parameters, and focus on the identification of novel variance components that improve the prognosis prediction beyond that achieved by the clinicopathologic features alone.

This allows the identification of low-variance probes that are unlikely to be informative in sex classification.

The identification of such variance-controlling loci, which we call vQTL, can be helpful in a variety of contexts, including selection of livestock for uniformity, evaluating predictability of response to medical treatment, identification of key biomolecular stabilizers, and assessment of population resilience in ecology and evolution.

Note, crucially, that even if we assume high experimental noise in the data, such noise would affect equally both Euclidean distributions and the identification of distinct variances for distances of metastatic aneuploidy cells and primary aneuploidy cells would be even more unlikely to be observed by chance.

A number of nomological networks between usability constructs were discovered, contributing to identification of sources of variances of purchase intention and purchase behavior.

We found that GMixBayes outperforms the regularized t-test and the GEO-adjusted methods in the identification of estimating prior variances from GEO database using Gaussian mixture model, and then integrates the priors with the data variances from differentially expression genes (DEGs) in gene expression microarray studies.

The G test allows positive identification of exceptionally low variances.

Addressing this challenge beyond the current state-of-the-art that are mostly concerned with identification algorithms, this work obtains closed form analytical expressions for the identification uncertainty (variance) of modal parameters that fundamentally explains the effect of test configuration.