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If there was more than one pregnancy in the last five years, the outcomes and predictors were based on their last pregnancy.
Model predictors were based on 300 ms events convolved with a hemodynamic response gamma function [25].
Several decades ago, most predictors were based on identifying maximally valued regions of sequences; essentially looking for peaks, or troughs, in some form of a propensity plot.
However, source estimates for different predictors were based on the same noise covariance matrices that were computed for baseline intervals of 200 ms duration before picture onset.
The predictors were based on the sets of genes that were found by the procedure that was described in the previous subsection, but in each iteration a different training set for inferring the weights of the different genes in the predictor was used; and in each iteration the predictors were implemented on different test set.
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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].
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].
The predictors are based on statistically significant lagged inputs and partitioned into training (80%) and testing (20%) subsets to construct the forecasting models.
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.
Further, selection of the predictors was based on a p value of 0.05, which is considered conservative in prediction modelling.
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CEO of Professional Science Editing for Scientists @ prosciediting.com