Sentence examples for genomic models that from inspiring English sources

More recent research has moved past permutations and toward multichromosomal genomic models that incorporate both linear and circular chromosomes.

A recent study by Yang et al. (2011) explored the use of parametric genomic models that specify genetic control of environmental variance in a swine production system.

"Survival risk group prediction" was adopted to construct a functional genomic model that used the IPF prognostic predictor gene set to derive a prognostic index (PI) for each patient into either high or low risk for survival outcomes.

The specific parameterization of the genomic model that results in the Bayes SSVS model is described below.

In this study, we constructed a functional genomic model that predicted survival in three independent cohorts of IPF patients.

Using supervised and unsupervised analyses, we identified a set of IPF prognostic predictor genes and derived a functional genomic model that predicted high and low-risk IPF patients with high accuracy.

Genomic selection relies on the prediction of genomic estimated breeding values (GEBV) of individuals or lines using marker genotype information only, by applying genomic prediction models that are based on training individuals that have both phenotypic and genotypic data.

Thus, it is concluded that genomic prediction models that use pre-computed SNP variances are efficient and can generate GEBV with improved properties, in terms of reliability and bias, compared to the commonly used RR-BLUP model.

Genomic prediction models that use pre-computed SNP variances proved to be able to generate GEBV with better properties, in terms of reliability and bias, than the commonly used RR-BLUP model.

Genomic prediction models that fit well have small values for c and result in greater relative emphasis of reliable information than is the case when the genomic prediction model fits poorly and the residual variation is dominated by contributions from lack-of-fit.

Moreover, genomic prediction models that assume non-normal distributions of effects in some cases give higher accuracies than GBLUP when very large numbers of SNPs (e.g. 630 K or whole-genome sequence data) are used, particularly for multi-breed and across-breed predictions [ 5, 18- 22].