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Paired site searches started out from individual site predictions adjusted to a P-value of 10-4.
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Crucially, when the predictions are adjusted to compensate for class priors in the test set, PPV improves in every case and the NPV of the adjusted classifier remains approximately equivalent to the non-adjusted classifier.
It relies on a link prediction method adjusted to the observations that exploits the statistical regularities of the empirical network to estimate connection probabilities, that afterwards are integrated at the level of reactions.
Model predictions were subsequently adjusted to derive concentrations for a nominal emission rate of 1 g km-1.
The time and temperature dependent fatigue life prediction model is adjusted to LCF tests.
Also, two of the prediction models are adjusted to our measurements.
We employed Gaussian process classifiers (GPCs) as the primary classification approach in this study because they are fully probabilistic prediction devices and therefore provide predictions that can be easily adjusted to compensate for variable class priors (see section Accommodating Variable Class Priors).
Indeed, several commercial packages exist that perform NMR prediction based on models adjusted to large databases of observed chemical shifts [1 9].
Because this research is about multi-target classification i.e. a data can belong to more than one category, the prediction model needs to be adjusted to cope with the multi-target problem.
Here, the grid size for spatial prediction needs to be reasonably adjusted to the existing sampling scheme.
Eleven parameters (4 τ's, 3 weights, 3 EC50's, and an additional basal receptor activity term) were adjusted to fit model predictions (see Additional file 1, Supplemental Methods).
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