AustralieFrV1, Main, Exploration, bibRecord, 008E51

Boosted regression trees, multivariate adaptive regression splines and their two-step combinations with multiple linear regression or partial least squares to predict blood-brain barrier passage : A case study

Identifieur interne : 008E51 ( Main/Exploration ); précédent : 008E50; suivant : 008E52

Boosted regression trees, multivariate adaptive regression splines and their two-step combinations with multiple linear regression or partial least squares to predict blood-brain barrier passage : A case study

Auteurs : E. Deconinck [Belgique] ; M. H. Zhang [République populaire de Chine] ; F. Petitet [France] ; E. Dubus [France] ; I. Ijjaali [France] ; D. Coomans [Australie] ; Y. Vander Heyden [Belgique]

Source :

Analytica chimica acta [ 0003-2670 ] ; 2008.

RBID : Pascal:08-0159243

Descripteurs français

Pascal (Inist)
- Analyse multivariable, Régression multiple, Régression linéaire, Régression PLS, Sang, Modélisation, Méthode adaptative, Médicament, Base de données, Relation structure activité, Spline, Barrière hématoencéphalique, Etude cas.
Wicri :
- topic : Médicament, Base de données.

English descriptors

KwdEn :
- Adaptive method, Blood, Blood brain barrier, Case study, Database, Drug, Linear regression, Modeling, Multiple regression, Multivariate analysis, PLS regression, Spline, Structure activity relation.

Abstract

The use of some unconventional non-linear modeling techniques, i.e. classification and regression trees and multivariate adaptive regression splines-based methods, was explored to model the blood-brain barrier (BBB) passage of drugs and drug-like molecules. The data set contains BBB passage values for 299 structural and pharmacological diverse drugs, originating from a structured knowledge-based database. Models were built using boosted regression trees (BRT) and multivariate adaptive regression splines (MARS), as well as their respective combinations with stepwise multiple linear regression (MLR) and partial least squares (PLS) regression in two-step approaches. The best models were obtained using combinations of MARS with either stepwise MLR or PLS. It could be concluded that the use of combinations of a linear with a non-linear modeling technique results in some improved properties compared to the individual linear and non-linear models and that, when the use of such a combination is appropriate, combinations using MARS as non-linear technique should be preferred over those with BRT, due to some serious drawbacks of the BRT approaches.

Affiliations:

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Le document en format XML

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<front><div type="abstract" xml:lang="en">The use of some unconventional non-linear modeling techniques, i.e. classification and regression trees and multivariate adaptive regression splines-based methods, was explored to model the blood-brain barrier (BBB) passage of drugs and drug-like molecules. The data set contains BBB passage values for 299 structural and pharmacological diverse drugs, originating from a structured knowledge-based database. Models were built using boosted regression trees (BRT) and multivariate adaptive regression splines (MARS), as well as their respective combinations with stepwise multiple linear regression (MLR) and partial least squares (PLS) regression in two-step approaches. The best models were obtained using combinations of MARS with either stepwise MLR or PLS. It could be concluded that the use of combinations of a linear with a non-linear modeling technique results in some improved properties compared to the individual linear and non-linear models and that, when the use of such a combination is appropriate, combinations using MARS as non-linear technique should be preferred over those with BRT, due to some serious drawbacks of the BRT approaches.</div>
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Boosted regression trees, multivariate adaptive regression splines and their two-step combinations with multiple linear regression or partial least squares to predict blood-brain barrier passage : A case study

Boosted regression trees, multivariate adaptive regression splines and their two-step combinations with multiple linear regression or partial least squares to predict blood-brain barrier passage : A case study

Source :

Descripteurs français

English descriptors

Abstract

Links toward previous steps (curation, corpus...)

Le document en format XML

Pour manipuler ce document sous Unix (Dilib)

Pour mettre un lien sur cette page dans le réseau Wicri