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Owing to the use of a robust classifier and an effectively feature extraction method, our prediction approach obtained good prediction results.
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Molecular dynamics simulation of wild-type hGBE1, hGBE1-Y329S and LTKE peptide-bound hGBE1-Y329S models (Fig. 5A; Supplementary Material, Methods) corroborated our prediction that LTKE stabilizes the mutated enzyme.
We further examined the performance of our prediction method for predicting non-consensus sites of sumoylation.
This result has a significant P-value 0.099 (see Materials and Methods), suggesting that our prediction is statistically significant.
To test if our method could identify RNA editing sites in other species, we applied our prediction method to the A. thaliana chloroplast genome.
Because these published methods use an equal number of binding and nonbinding residues, we applied our prediction method to a similar dataset to make the results comparable.
In order to validate our prediction method, we compared the ROCs for the best four 5-marker panel predictions determined by our method with the ROCs for four randomly chosen 5-marker panels from 42 candidate biomarkers.
Our prediction method offers the potential to detect elderly individuals with apparently normal cognition at risk of imminent cognitive decline.
We then use three selected applications to check the accuracy of our prediction method by comparing our estimations with the corresponding measurements obtained using a multimeter.
Our prediction method is based on a random forest classifier and utilizes a set of complementary network centrality measures.
We test these hypotheses and obtain a set of candidate measures which are the basis for our prediction method described in Section 5.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com