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But in the last decade of the 20th century, developments in information technology began to erode that model's power base.
Two sets of scenarios demonstrate the model's power for scenario exploration.
The relatively dinky processor can't match the TI model's power.
Second, the iPhone's ARMv6-based processor can't match the TI model's power.
In modelling functional mapping, we usually estimate the structured form of the variance-covariance matrix to increase the model's power rather than estimate every element in the matrix.
Similarly, the model's power with respect to features ϕ0 and ϕ1 is 0, hence those features are not detectable at the given sample size.
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The quality of such reverse-engineered GRN models is determined mainly by two factors: The quality of a GRN model depends on two factors: the model's explanatory power (or model completeness) and the model's predictive power (or model correctness).
This might also have partially disadvantaged the model's prediction power, as the V3 region is known to indirectly influence disease progression rates.
Similarly, the model's discriminatory power (measured by AUC) of the two other modelling algorithms yielded values of 0.89 for Support Vector Machines (SVM) and 0.99 for Environmental distance.
Cross-validation verification of each analysis confirmed the model's predictive power.
And while the model's predictive power was positive, it was low with an Adjusted R-squared of 0.1494.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com