Your English writing platform
Discover LudwigExact(6)
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].
The predictors are based on statistically significant lagged inputs and partitioned into training (80%) and testing (20%) subsets to construct the forecasting models.
However, in most cases, the predictors are based on molecular signatures with low functional value per se.
In this paper, we use an enhanced L1/2 penalized solver to penalize network-constrained logistic regression model called an enhanced L1/2 net, where the predictors are based on gene-expression data with biologic network knowledge.
Inspired by the aforementioned methods and ideas, here, we define a network-constrained logistic regression model with L1/2 penalty following the framework established by [ 11], where the predictors are based on the gene-expression data with biologic network knowledge.
In contrast, the estimated HR's for the REGS predictors are based on an increase of 10 in the predicted AUC 0. For each patient of IDRC, LLMPP, and MDFCI the HBCCL based REGS predictor for each drug was used to assign a resistance index towards each of the three drugs.
Similar(54)
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].
If there was more than one pregnancy in the last five years, the outcomes and predictors were based on their last pregnancy.
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.
Model predictors were based on 300 ms events convolved with a hemodynamic response gamma function [25].
The core engine of ESS++ for the selection of relevant predictors is based on Evolutionary Monte Carlo.
Write better and faster with AI suggestions while staying true to your unique style.
Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com