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Orthogonal PLS (OPLS) Modeling for Improved Analysis and Interpretation in Drug Design
Umeå University, Faculty of Science and Technology, Department of Chemistry. (Computational Life Science Cluster (CLIC))
2012 (English)In: Molecular informatics, ISSN 1868-1751, Vol. 31, no 6-7, 414-419 p.Article in journal (Refereed) Published
Abstract [en]

Partial least squares (PLS) regression is a flexible data analytical approach, which can be made even more versatile and useful by various modifications. In this article we describe the extension into orthogonal PLS modeling, in terms of two new methods, called OPLS and O2PLS, with similar prediction capacity but improved model interpretation.

Place, publisher, year, edition, pages
Weinheim: Wiley-VCH Verlagsgesellschaft, 2012. Vol. 31, no 6-7, 414-419 p.
Keyword [en]
Latent variables, Predictive variation, Orthogonal variation, Interpretability
National Category
Chemical Sciences
Identifiers
URN: urn:nbn:se:umu:diva-54362DOI: 10.1002/minf.201200158OAI: oai:DiVA.org:umu-54362DiVA: diva2:517886
Note

Article first published online: 16 APR 2012

Available from: 2012-04-24 Created: 2012-04-24 Last updated: 2012-10-16Bibliographically approved

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Trygg, Johan

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