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We compared the results obtained from MUSCLE alignments before and after trimming with trimAl using automated selection of parameters.
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The objective of this study was to determine the reproducibility of logistic regression models developed using automated variable selection methods.
We fitted multiple predictor models using automated model selection via information theoretic approaches and multi-model averaging using maximum likelihood.
One of the first trials reported using automated tube potential selection was performed for the imaging of the great vessels; in this case, it was possible to lower radiation exposure by 25.1% [ 18].
A limited number of studies have investigated radiation exposure while using automated tube potential selection [ 12]–[ 16],[ 18].
But Trulia's McLaughlin insists that using automated estimates of value produces a more accurate picture.
Logistic regression models were fitted to the data using automated and manual selection methods.
The approach is based on using Taguchi's orthogonal arrays, combined with automated selection of sensory characteristic features, to provide economically effective and optimal selection of sensors and signal processing methods with reduced experimental work.
We plotted burst onsets across all channels and found that burst onsets were visibly clustered across channels (Supplementary Fig. 3), enabling an automated selection of multi-channel bursts using a simple threshold.
CT examinations were performed on the experimental group using a 128-slice CT machine (SOMATOM Definition Flash, Siemens, Forchheim, Germany), using automated attenuation-based tube potential selection (CAREkV).
On each bootstrap sample, we applied three most commonly used automated model selection procedures (forward, backward and stepwise) where all candidate variables are included at the initial stage of the analysis.
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