Désirée Baumann

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Background: Generally, QSAR modelling requires both model selection and validation since there is no a priori knowledge about the optimal QSAR model. Prediction errors (PE) are frequently used to select and to assess the models under study. Reliable estimation of prediction errors is challenging – especially under model uncertainty – and requires(More)
In most cases of QSAR modelling the final model used to make predictions, is not known a priori but has to be selected in a data driven fashion (e.g. selection of principal components, variable selection, selection of the best mathematical modelling technique). Reliable estimation of externally validated prediction errors under this model uncertainty is(More)
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