Sentence examples for models with genetic from inspiring English sources

In quantitative genetics, Markov chain Monte Carlo (MCMC) methods are indispensable for statistical inference in non-standard models like generalized linear models with genetic random effects or models with genetically structured variance heterogeneity.

Until recently, our laboratories and others have developed mouse models with genetic deficiencies in glutamate-cysteine ligase (GCL), the rate-limiting enzyme in the GSH biosynthetic pathway.

The integration of global metabolic models with genetic and regulatory models will be essential for the practice of metabolic engineering for strain improvement to move forward, simply because we cannot rely on our intuition to grasp the complexity of the biological systems involved.

The proposed model has been the result of coupling two RSM models with genetic algorithm.

For example, in the models with genetic alterations, although the proteins encoded by the genes of human and rodents are highly homologous, the regulation of gene transcription by transcription factors or epigenetics, or the regulation of gene translation by microRNAs, can vary extensively between humans and rodents.

In contrast, GAD65 dysfunction may be the consequence of a predominant vulnerability of neuronal pathways in the amygdala-hippocampal complex, according to the findings in the animal models with genetic modification of GAD65 expression (15 26; see above).

Next, an optimization model with genetic algorithm method is developed to optimize the process.

For better accurate forecasting of port throughput, a back propagation neural network model with genetic algorithms is proposed.

In response to these two extremes, the authors propose a new approach called Meta-Parametric Design, combining graph-based parametric modelling with genetic programming.

The prediction accuracy of optimized neural network model with genetic algorithm had a root mean-squared error of 0.03 nm/min.

The experimental results obtained from a statistical experimental design were used for the modeling and optimization by linking an artificial neural network (ANN) model with genetic algorithm (GA) in MATLAB.