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A recently proposed solution to increase quality of traditional predictors is based on filtering predictions of sequence-based methods using profiling of miR and mRNA expressions [ 16].
Studies show the admissibility of simultaneous prediction in the class of nonhomogeneous linear predictors is based on the admissibility of simultaneous prediction in the class of homogeneous linear predictors.
The core engine of ESS++ for the selection of relevant predictors is based on Evolutionary Monte Carlo.
The choice of the disorder predictors is based on the results from a recent review (Kurgan and Disfani, 2011).
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Current traditional sequence-based target predictors are based on the presence of a conserved 'seed region' (nucleotides 2 7) of exact Watson-Crick complementary base-pairing between the 3' UTR of the mRNA and the 5' end of the miR [ 15, 16].
If there was more than one pregnancy in the last five years, the outcomes and predictors were based on their last pregnancy.
The predictors are based on statistically significant lagged inputs and partitioned into training (80%) and testing (20%) subsets to construct the forecasting models.
Model predictors were based on 300 ms events convolved with a hemodynamic response gamma function [25].
Further, selection of the predictors was based on a p value of 0.05, which is considered conservative in prediction modelling.
However, in most cases, the predictors are based on molecular signatures with low functional value per se.
The inclusion of these predictors was based on past theory and research, and it was predicted that all of them would be associated with mental health outcomes.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com