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In considering approaches to validation, achieving regulatory acceptance of toxicogenomics-based methods or acceptance of information/data derived from such methods is an important goal.
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By using different data collection procedures, the researcher is able to increase the validity and robustness of results because the findings can be strengthened by cross validation achieved by using data obtained through different strategies [ 37].
The results of the empirical comparison indicate an agent-based modeling approach would achieve a characterization model better with validation achieved through an event-based approach.
The best model, evaluated using 10-fold cross validation, achieved an AUC score of over 98%%.
This validation achieved correct diagnosis on all the 281 images of OraQuick ADVANCE Rapid HIV-1/2 antibody tests for up to even 5× dilution factors.
Measurements of the three predictive phage proteins were combined in a logistic regression model that achieved 80% sensitivity and 100% specificity in prediction of sample status, whereas leave-one-out validation achieved 77.0% sensitivity and 82.8% specificity among 87 patient samples and 87 control samples.
A Bernoulli Naïve Bayes algorithm was trained using the data and evaluated using fivefold cross-validation, achieving a mean recall and precision of 67.7 and 63.8 % for active compounds and 99.6 and 99.7 % for inactive compounds, respectively.
The experimental results on 762 field moth samples by 10-fold cross-validation achieved a good identification accuracy of 96.9%, and indicated that the deployment of the proposed pose estimation process is effective for automated moth identification.
Leave-one-out cross-validation demonstrates prediction errors of <15% at the common legal limit for intoxication (17.4 mmol/L = 0.08% by vol) and the best blind cross-validation achieves <12% error at this concentration.
The cross-validation achieved a classification result of 55.6%.
A threefold cross-validation achieved highly similar MCCs as the original model.
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