Sentence examples for models by genetic from inspiring English sources

As prerequisites for the generation of appropriate disease models by genetic engineering, pigs exhibit suitable reproductive performance traits, pig genome analyses resulted in the availability of several resources of genomic data, and efficient and precise techniques for the genetic modification of pigs have been established.

Postmortem findings of imbalances between the direct and indirect striatal output pathways led to mechanistic hypotheses that are now being directly tested in animal models by genetic engineering to enable cell-type-specific expression of opsins, DREADDs (designer receptors exclusively activated by designer drugs), calcium indicators, and disease gene constructs.

As prerequisites for the generation of appropriate disease models by genetic engineering, pigs show suitable reproductive performance traits, a high-quality draft pig genome sequence is available, and efficient and precise techniques for the genetic modification of pigs have been established.

To date, there have been few studies that examine the role of LRP1 in vivo using animal models by genetic manipulation of LRP1 expression.

If B cells are removed from lupus models by genetic manipulations or chronic antibody therapy, the syndrome is largely suppressed, including T-cell abnormalities [ 5].

In this section, a differential evolution algorithm proposed by Gholaminezhad and Jamali [31] is used for optimization of operational variables of RSOC based on the obtained optimum model by genetic programming.

Parameters of SCC mixes modelled by genetic programming were the slump flow, JRing combined to the Orimet, JRing combined to cone, and the compressive strength at 7, 28 and 90 days.

We tested the effects TGF-β and hypoxia on bone metastases in this model by genetic and pharmacologic approaches.

The aim of this study is to assess the potential of hydrological models derived by genetic programming (GP) to estimate runoff at ungauged catchments by regionalization.

Our approach can compare prediction models induced by genetic data, clinical data, and both of them and can estimate the contribution of genetic factors for the last model.

All models obtained by genetic algorithm procedures were consistent with a correct classification, higher than 90%, restricting to 30 variables in both classification methods applied.