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The most parsimonious model under SH was a model including genetic dominance, CT, and non-shared environmental factors.
A clear advantage in using the C6 glioma model is the extensive characterization and the literature produced using this model, including genetic analysis (Grobben et al., 2002).
A model including genetic diversity and environmental stress explained much of variation in MPS during the chronic low salinity experiment (Phase 2) (adjusted R2 = 0.74, p < 0.0001).
Based on test statistics, the PA model including genetic dominance (but not CT) fitted the data relatively better than the PA model including negative CT (but not genetic dominance).
Furthermore, a model including genetic recombination of phage genomes (but not of CRISPR loci) shows that recombination may allow phages to escape CRISPR recognition more effectively than does point mutation alone (Han et al. 2013).
Under PA, we ended up with two alternative models as the estimates of genetic dominance and negative CT were confounded: (i) a model including negative CT but not genetic dominance, or (ii) a model including genetic dominance but not negative CT.
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The basic model included genetic variables with P values < 0.15 obtained in univariate analyses.
The second model included genetic relationships but ignored covariances between cycles.
This would be the case even if the true underlying model includes genetic heterogeneity of residual variance, which was confirmed in a simulation study with 200 families of 40 individuals resulting from a perfect 2 sires × 2 dams mating system of 100 sires and 100 dams (results not shown).
In this paper, a comparison between two types of bio-inspired computational intelligence models including genetic programming (GP) and artificial neural network (ANN) models was implemented to estimate the velocity field within a constructed wetland (i.e., the Stormwater Treatment Area in South Florida) in the Everglades, Florida.
In the present work, the quantitative structure activity relationship (QSAR) models, including genetic function approximation (GFA) and least squares support vector machine (LSSVM) were developed for predicting the ecotoxicity of ILs towards the marine bacterium Vibrio fischeri based on the descriptors calculated from cations and anions.
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