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In an attempt to maximize prediction sensitivity, some efforts consider many interactions that lack a canonical 7 8 nt 3′-UTR site.
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These models maximize prediction capacity with the coefficients and variables functioning as a set.
To fully maximize prediction performance, a suite of machine learning classifiers was tested on the WEKA machine learning platform.
The aim was to maximize both sensitivity and negative predictive values.
Considering the numbers of binding and non-binding residues in proteins are significantly unequal, two additional weights as well as SVM parameters are analyzed and adopted to maximize net prediction (NP, an average of sensitivity and specificity) accuracy.
But the telescope was designed to maximize overall sensitivity and tracked only one frequency.
These features maximize the sensitivity to damage and accuracy of damage localization.
Numerous factors affecting the reduction peak currents of folic acid at PMo12-PPy/GCE were optimized to maximize the sensitivity.
To maximize the sensitivity, the thresholds were set in consideration of the noise level of measurement.
To maximize phase sensitivity, no lateral scanning was performed.
This approach was chosen to maximize diagnostic sensitivity.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com