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We then tested the predictive capability of the models by predicting 30-day sorption/desorption data using kinetic parameters fitted on 1-day sorption/desorption data only.
As with eve 2, we can further compare the models by predicting the outcomes of regulatory perturbations of input TFs.
Validation of metabolic models by predicting gene essentiality is a routine procedure and the frequency of true positives is a measure of the network's quality.
The models were developed using data for 232 structurally diverse chemicals (training set) with a 10 6) range of relative binding affinities (RBAs); we then validated the models by predicting ER RBAs for 463 chemicals that had ER activity data (testing set).
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We tested the predictive performance of our model by predicting the most recent BMI based on the previous one, among patients with CPRD with more than one recorded BMI available.
Evaluate the performance of the constructed model by predicting the target activities of the compounds in the test dataset that are not used for model construction. .
Evaluate the performance of the constructed model by predicting the target activities of the compounds in the test dataset that are not used for model construction.
We validated the model by predicting the observed patch-size distribution and patch patterns from 2002 to 2010 where data were available.
We demonstrate this model by predicting the mechanical response of bacterial nanocellulose hydrogel composites, which are promising biomaterials and a structural mimetic for the plant cell wall.
We used a niche reduction approach to species distribution modelling by predicting the historic and current distributions of a critically endangered Australian rodent, the central rock-rat (CRR).
We demonstrate the accuracy and robustness of the model by predicting single component adsorption of CO2, methane and other relevant components under a range of conditions.
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