ArticleJournal of cheminformatics2014
Reliable estimation of prediction errors for QSAR models under model uncertainty using double cross-validation.
Article in Journal of cheminformatics, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 39 papers, 1 of them a synthesis that pooled it.
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Who cites it
39 citing papers in PubMed, 1 synthesis or guideline pooled it.
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- Cross validated serum small extracellular vesicle microRNAs for the detection of oropharyngeal squamous cell carcinoma.Journal of translational medicine · 2020Article
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- Validation strategies for target prediction methods.Briefings in bioinformatics · 2020Review
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2 authors.
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Abstract
backgroundGenerally, QSAR modelling requires both model selection and validation since there is no
methodsSimulated and real data are analysed with double cross-validation to identify important factors for the resulting model quality. For the simulated data, a bias-variance decomposition is provided.
resultsThe prediction errors of QSAR/QSPR regression models in combination with variable selection depend to a large degree on the parameterization of double cross-validation. While the parameters for the inner loop of double cross-validation mainly influence bias and variance of the resulting models, the parameters for the outer loop mainly influence the variability of the resulting prediction error estimate.
conclusionsDouble cross-validation reliably and unbiasedly estimates prediction errors under model uncertainty for regression models. As compared to a single test set, double cross-validation provided a more realistic picture of model quality and should be preferred over a single test set.
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