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Compared to engineering or physics problems, dynamical models in quantitative biology typically depend on a relatively large number of parameters.
At the same time, we also urge more careful consideration of the role of TP models in quantitative risk assessment and the likelihood of their success in fulfilling this role.
Structural Equation Models (SEM) can be used to study recursive and simultaneous relationships among phenotypes in multivariate systems such as genetical genomics, system biology, and multiple trait models in quantitative genetics.
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The effectiveness of a terrestrial laser scanning (TLS) model in quantitative stability analysis is also demonstrated.
The importance of measuring quality of a conceptual model in quantitative terms has been emphasized in the research but still the quantitative measures are very scarce in the literature.
A wavelet neural network (WNN) model in quantitative structure property relationship (QSPR) was developed for predicting solubility of 25 anthraquinone dyes in supercritical carbon dioxide over a wide range of pressures (70 770 bar) and temperatures (291 423 K).
A multiple linear model was used to test for difference between genotype groups assuming an additive model in quantitative trait studies.
Our model is built on the classic mixed effect model in quantitative genetics (Henderson 1984).
The role of mathematics and modeling in quantitative biology is to predict the behavior of a system.
The classical inheritance model in quantitative genetics divides the breeding value into parent average and a Mendelian sampling term.
Our data further suggest that the simplifying assumption of the use of the infinitesimal model in quantitative genetic theory may be unrealistic.
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