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In the test of frameworks, superior coincidence is demonstrated using the parametric framework.
Trends in selected gene contents were also observed using the parametric framework.
Using the parametric framework, a larger overlap and smaller disagreement between the lists for each platform are obtained.
This is evidenced by the greater coincidence achieved between data acquired using different platforms when analyzed using the parametric framework.
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In this work, we propose using the parametric model (1) within the framework of inverse problems and a Bayesian approach to predict genetic values of individuals when p > > n.
Capwire calculates 95% CIs using the parametric bootstrap [27].
In the parametric framework, chip data are normalized using a distribution model as the standard.
In the parametric framework, only data in the valid range were used for selection.
Clearly, generic approximation spaces are unnecessarily rich and hence unnecessarily expensive within the parametric framework.
The parametric framework appears to provide superior reproducibility with greater testing power and a lower false-positive rate compared to existing frameworks.
For simplicity, we use the parametric exponential inverse demand function f(x)= exp(−γ x); alternative inverse demand functions can also be implemented within our framework.
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