Classification of drugs in absorption classes using the classification and regression trees (CART) methodology

Deconinck, Éric; Hancock, Tim; Coomans, Danny; Massart, D.L.; Heyden, Yvan Vander

doi:10.1016/j.jpba.2005.03.008

Cited by 119 publications

(88 citation statements)

References 23 publications

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“…In the third and final step the optimal tree is selected using a cross validation procedure [25][26][27].…”

Section: Classification and Regression Trees (Cart)mentioning

confidence: 99%

“…Every split is defined by one value of one explanatory variable. For continuous explanatory variables the splits are defined by "x i < a j " where x i is the selected variable and a j its split value [25][26][27].…”

Section: Building the Maximum Treementioning

confidence: 99%

“…where i is the impurity, S the candidate split value, and p L and p R the fractions of the objects in the left and right daughter groups, respectively [25][26][27].…”

Section: Building the Maximum Treementioning

confidence: 99%

“…The different sub trees with the same complexity are then compared to find the optimal. This comparison is based on a cost-complexity measure ) (T R α , in which both tree accuracy and complexity are considered [25][26][27]. For each sub tree complexity T it is defined as:…”

Section: Tree Pruningmentioning

confidence: 99%

“…The selection is usually based on the evaluation of the predictive error of the models using a cross validation procedure. In this paper a 10-fold cross validation procedure [26,28] was used. The predictive error is then given as the overall misclassification rate for each of the sub trees [25].…”

Section: Selection Of the Optimal Treementioning

confidence: 99%

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Classification trees based on infrared spectroscopic data to discriminate between genuine and counterfeit medicines

Deconinck

Sacré

Coomans

et al. 2012

Journal of Pharmaceutical and Biomedical Analysis

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Abstract:Classification trees built with the Classification And Regression Tree algorithm were evaluated for modelling infrared spectroscopic data in order to discriminate between genuine and counterfeit drug samples and to classify counterfeit samples in different classes following the RIVM classification system. Models were built for two data sets consisting of the Fourier Transform Infrared spectra, the Near Infrared spectra and the Raman spectra for genuine and counterfeit samples of respectively Viagra ® and Cialis ® .Easy interpretable models were obtained for both models. The models were validated for their descriptive and predictive properties. The predictive properties were evaluated using both cross validation as an external validation set. The obtained models for both data sets showed a 100% correct classification for the discrimination between genuine and counterfeit samples and 83.3% and 100% correct classification for the counterfeit samples for the Viagra ® and the Cialis ® data set respectively.

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“…In the third and final step the optimal tree is selected using a cross validation procedure [25][26][27].…”

Section: Classification and Regression Trees (Cart)mentioning

confidence: 99%

Section: Building the Maximum Treementioning

confidence: 99%

“…where i is the impurity, S the candidate split value, and p L and p R the fractions of the objects in the left and right daughter groups, respectively [25][26][27].…”

Section: Building the Maximum Treementioning

confidence: 99%

Section: Tree Pruningmentioning

confidence: 99%

Section: Selection Of the Optimal Treementioning

confidence: 99%

See 3 more Smart Citations

Classification trees based on infrared spectroscopic data to discriminate between genuine and counterfeit medicines

Deconinck

Sacré

Coomans

et al. 2012

Journal of Pharmaceutical and Biomedical Analysis

Self Cite

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Recent Advances in Development, Validation, and Exploitation ofQSARModels

Tropsha

2010

Burger's Medicinal Chemistry and Drug Discovery

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Counting more than 45 years of intensive development and applications, QSAR modeling remains one of the major computational approaches for drug discovery and environmental chemical risk assessment. Recent developments in the field reflect the burgeoning growth of bioactivity and chemical databases. This growth provides a never‐ending impetus for the development of a growing body of QSAR models and their application for virtual screening to prioritize chemicals for experimental investigations. This chapter reviews recent and emerging trends in the QSAR modeling field with the emphasis on new approaches to chemical and biological data curation, model validation, virtual screening, and potential application of QSAR models by federal agencies for regulatory decision making.

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Recent Advances in Development, Validation, and Exploitation of QSAR Models

Tropsha

2021

Burger's Medicinal Chemistry and Drug Discovery

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Counting more than 45 years of intensive development and applications, QSAR modeling remains one of the major computational approaches for drug discovery and environmental chemical risk assessment. Recent developments in the field reflect the burgeoning growth of bioactivity and chemical databases. This growth provides a never‐ending impetus for the development of a growing body of QSAR models and their application for virtual screening to prioritize chemicals for experimental investigations. This article reviews recent and emerging trends in the QSAR modeling field with the emphasis on new approaches to chemical and biological data curation, model validation, virtual screening, and potential application of QSAR models by federal agencies for regulatory decision making.

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Classification of drugs in absorption classes using the classification and regression trees (CART) methodology

Cited by 119 publications

References 23 publications

Classification trees based on infrared spectroscopic data to discriminate between genuine and counterfeit medicines

Classification trees based on infrared spectroscopic data to discriminate between genuine and counterfeit medicines

Recent Advances in Development, Validation, and Exploitation ofQSARModels

Recent Advances in Development, Validation, and Exploitation of QSAR Models

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