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We propose a novel model predictive control scheme using a reduced order model for prediction in combination with an error bounding system.
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To explore the mechanism of miR-411-5p/3p promotiNSCLCCLC progress at the molecule level, miRWalk 2.0 is used for potential target genes prediction in combination with two sets of lung cancer microarray data (GSE51852 and GSE19188).
The new scheme employs linear prediction in combination with median filtering.
These theoretical predictions, in combination with experimental data, could be a great asset for the identification of ligand recognition sites and to the drug discovery process.
New techniques to determine parameters necessary for SIT prediction, especially in combination with Methodology III, have the power to reduce the costs for estimating the SIT up to 80%.
We conclude that genome-wide prediction methods in combination with permutation tests can be employed for analysis of introgression populations.
While interface predictions have been used previously in docking, their success has been mostly limited to cases for which interface prediction is relatively easy, such as rigid enzymes [11] or enzyme-inhibitors [3] In the current work, by using a consensus prediction strategy in combination with improved docking protocols, much improvement has been made over earlier attempts.
The initial set of ORFs was manually selected from the prediction result in combination with BLASTP and FramePlot results.
Therefore, to test the value of IL-6 for the improving risk prediction we calculated the NRI for IL-6 in combination with CRB-65 score and compared it with CRB-65 score alone and found a significant net gain in reclassification by including IL-6.
We could expand predictions for different therapeutic strategies in combination with radiation.
Assessment of such parameters (SNPs) has the potential for predicting adverse drug reactions and for obtaining a more accurate prediction of treatment response in combination with the profile of the tumour.
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