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The model that optimally explained the data consisted of a single effect for the step factor, b = -0.941, p <.001.
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After termination of the EM algorithm, the mixture optimally explains the data in terms of having generated it.
The objective of the optimization is to determine the set of model parameters, Θ, that optimally explains the input data, D. Θ depends on the thermodynamic model.
The goal of our optimization problem is to infer the activity of TFs as a function of cultivation parameters, such that we can optimally explain the regression coefficients, which were distilled from the observed gene expression data.
In this study, we use the TFInfer approach (4, 65), a Bayesian statistical method that integrates gene expression data with regulon information (collected from online databases such as Regulon DB or Ecocyc) to identify transcription factor (TF) activity profiles that optimally explain the measured changes in gene expression.
The model explains the data very well.
Otherwise, multiple scenarios can explain the data.
The issue addressed in this paper is to identify the best site parameters that optimally explain, and therefore predict, the actual site-specific amplification factor.
SVM performs classification by constructing an N-dimensional hyper plane that optimally separates the data into two categories.
As data is added, the generated categories are constantly modified and sharpened in order to optimally represent the data.
The 'estimated degrees of freedom' (edf) that most optimally fit the data was selected from a cross validation routine implemented in the mgcv-package.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com