Literature DB >> 15669704

Three-block bi-focal PLS (3BIF-PLS) and its application in QSAR.

L Eriksson1, J Damborsky, M Earll, E Johansson, J Trygg, S Wold.   

Abstract

When X and Y are multivariate, the two-block partial least squares (PLS) method is often used. In this paper, we outline an extension addressing a special case of the three-block (X/Y/Z) problem, where Z sits "under" Y. We have called this approach three-block bi-focal PLS (3BIF-PLS). It views the X/Y relationship as the dominant problem, and seeks to use the additional information in Z in order to improve the interpretation of the Y-part of the X/Y association. Two data sets are used to illustrate 3BIF-PLS. Example I relates to single point mutants of haloalkane dehalogenase from Sphingomonas paucimobilis UT26 and their ability to transform halogenated hydrocarbons, some of which are found as organic pollutants in soil. Example II deals with soil remediation capability of bacteria. Whole bacterial communities are monitored over time using "DNA-fingerprinting" technology to see how pollution affects population composition. Since the data sets are large, hierarchical multivariate modelling is invoked to compress data prior to 3BIF-PLS analysis. It is concluded that the 3BIF-PLS approach works well. The paper contains a discussion of pros and cons of the method, and hints at further developmental opportunities.

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Year:  2004        PMID: 15669704     DOI: 10.1080/10629360412331297452

Source DB:  PubMed          Journal:  SAR QSAR Environ Res        ISSN: 1026-776X            Impact factor:   3.000


  2 in total

1.  Megavariate analysis of environmental QSAR data. Part II--investigating very complex problem formulations using hierarchical, non-linear and batch-wise extensions of PCA and PLS.

Authors:  Lennart Eriksson; Patrik L Andersson; Erik Johansson; Mats Tysklind
Journal:  Mol Divers       Date:  2006-06-27       Impact factor: 2.943

2.  Improving stability and understandability of genotype-phenotype mapping in Saccharomyces using regularized variable selection in L-PLS regression.

Authors:  Tahir Mehmood; Jonas Warringer; Lars Snipen; Solve Sæbø
Journal:  BMC Bioinformatics       Date:  2012-12-08       Impact factor: 3.169

  2 in total

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