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Authors: J. E. Salazar Jiménez 1 ; J. D. Sánchez Carvajal 1 ; B. Quiros-Gómez 2 and J. D. Arias-Londoño 3

Affiliations: 1 Faculty of Engineering and Universidad de Antioquia, Colombia ; 2 Unidad de Investigación e Innovaci´on and Humax Pharmaceutical S.A., Colombia ; 3 Universidad de Antioquia, Colombia

ISBN: 978-989-758-214-1

Keyword(s): Automatic Feature Selection, Bioequivalence, Drug Development, Drug Dissolution Profile Prediction, Solid Oral Pharmaceutical Forms.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Bioinformatics ; Biomedical Engineering ; Computational Intelligence ; Data Mining and Machine Learning ; Pharmaceutical Applications ; Soft Computing

Abstract: This work addressed the problem of dimensionality reduction in the drug dissolution profile prediction task. The learning problem is assumed as a multi-output learning task, since dissolution profiles are recorded in non-uniform sampling times, which avoid the use of basic function-on-scalar regression approaches. Ensemblebased tree methods are used for prediction, and also for the selection of the most relevant features, because they are able to deal with high dimensional feature spaces, when the number of training samples is small. All the drugs considered corresponds to rapid release solid oral pharmaceutical forms. Six different feature selection schemes were tested, including sequential feature selection and genetic algorithms, along with a feature scoring procedure, which was proposed in order to get a consensus about the best subset of variables. The performance was evaluated in terms of the similitude factor used in the drug industry for dissolution profile comparison . The feature selection methods were able to reduce the dimensionality of the feature space in 79.2%, without loss in the performance of the prediction system. The results confirm that in the dissolution profile prediction problem, especially for different solid oral pharmaceutical forms, variables from different components and phases of the drug development must be considered. (More)

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Paper citation in several formats:
Salazar Jiménez J., Sánchez Carvajal J., Quiros-Gómez B. and Arias-Londoño J. (2017). Automatic Feature Selection in the SOPFs Dissolution Profiles Prediction Problem.In Proceedings of the 10th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 3: BIOINFORMATICS, (BIOSTEC 2017) ISBN 978-989-758-214-1, pages 52-58. DOI: 10.5220/0006141800520058

@conference{bioinformatics17,
author={J. E. Salazar Jiménez and J. D. Sánchez Carvajal and B. Quiros-Gómez and J. D. Arias-Londoño},
title={Automatic Feature Selection in the SOPFs Dissolution Profiles Prediction Problem},
booktitle={Proceedings of the 10th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 3: BIOINFORMATICS, (BIOSTEC 2017)},
year={2017},
pages={52-58},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006141800520058},
isbn={978-989-758-214-1},
}

TY - CONF

JO - Proceedings of the 10th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 3: BIOINFORMATICS, (BIOSTEC 2017)
TI - Automatic Feature Selection in the SOPFs Dissolution Profiles Prediction Problem
SN - 978-989-758-214-1
AU - Salazar Jiménez J.
AU - Sánchez Carvajal J.
AU - Quiros-Gómez B.
AU - Arias-Londoño J.
PY - 2017
SP - 52
EP - 58
DO - 10.5220/0006141800520058

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