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Applicability domain dependent predictive uncertainty in QSAR regressions
Linnaeus University, Faculty of Health and Life Sciences, Department of Biology and Environmental Science. Centre of Environmental and Climate Research, Lund University, Lund.
Ideaconsult Ltd, Sofia, Bulgaria.
Linnaeus University, Faculty of Health and Life Sciences, Department of Biology and Environmental Science.ORCID iD: 0000-0001-9382-9296
2014 (English)In: Molecular Informatics, ISSN 1868-1751, Vol. 33, no 1, 26-35 p.Article in journal (Refereed) Published
Abstract [en]

Predictive models used in decision making, such as QSARs in chemical regulation or drug discovery, call for evaluated approaches to quantitatively assess associated uncertainty in predictions. Uncertainty in less reliable predictions may be captured by locally varying predictive errors. In the current study, model-based bootstrapping was combined with analogy reasoning to generate predictive distributions varying in magnitude over a model’s domain of applicability. A resampling experiment based on PLS regressions on four QSAR data sets demonstrated that predictive errors assessed by k nearest neighbour or weighted PRedicted Error Sum of Squares (PRESS) on samples of external test data or by internal cross-validation improved the performance of the uncertainty assessment. Analogy using similarity defined by Euclidean distances, or differences in standard deviation in perturbed predictions, resulted in better performances than similarity defined by distance to, or density of, the training data. Locally assessed predictive distributions had on average at least as good coverage as Gaussian distribution with variance assessed from the PRESS. An R-code is provided that evaluates performances of the suggested algorithms to assess predictive error based on log likelihood scores and empirical coverage graphs, and which applies these to derive confidence intervals or samples from the predictive distributions of query compounds.

Place, publisher, year, edition, pages
2014. Vol. 33, no 1, 26-35 p.
National Category
Environmental Sciences
Research subject
Natural Science, Environmental Science
Identifiers
URN: urn:nbn:se:lnu:diva-28032DOI: 10.1002/minf.201200131ISI: 000346768100004Scopus ID: 2-s2.0-84895165560OAI: oai:DiVA.org:lnu-28032DiVA: diva2:639899
Available from: 2013-08-11 Created: 2013-08-11 Last updated: 2016-11-15Bibliographically approved

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Sahlin, UllrikaÖberg, Tomas

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CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf