Dimensionality reduction, and function approximation of poly(lactic-co-glycolic acid) micro- and nanoparticle dissolution rate

Varun Kumar Ojha,1,2 Konrad Jackowski,3 Ajith Abraham,1,4 Václav Sná&scaron;el1,2 1IT4Innovations, V&Scaron;B – Technical University of Ostrava, Ostrava, Czech Republic; 2Department of Computer Science, V&Scaron;B – Technical University of Ostr...

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Autores principales:	Ojha VK, Jackowski K, Abraham A, Snášel V
Formato:	article
Lenguaje:	EN
Publicado:	Dove Medical Press 2015
Materias:	Medicine (General) R5-920
Acceso en línea:	https://doaj.org/article/a9627258154f4b7aaadf7a3aab9a6663
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spelling	oai:doaj.org-article:a9627258154f4b7aaadf7a3aab9a66632021-12-02T05:10:44ZDimensionality reduction, and function approximation of poly(lactic-co-glycolic acid) micro- and nanoparticle dissolution rate1178-2013https://doaj.org/article/a9627258154f4b7aaadf7a3aab9a66632015-02-01T00:00:00Zhttp://www.dovepress.com/dimensionality-reduction-and-function-approximation-of-polylactic-co-g-peer-reviewed-article-IJNhttps://doaj.org/toc/1178-2013 Varun Kumar Ojha,1,2 Konrad Jackowski,3 Ajith Abraham,1,4 Václav Sná&scaron;el1,2 1IT4Innovations, V&Scaron;B – Technical University of Ostrava, Ostrava, Czech Republic; 2Department of Computer Science, V&Scaron;B – Technical University of Ostrava, Ostrava, Czech Republic; 3Department of Systems and Computer Networks, Wroclaw University of Technology, Wroclaw, Poland; 4Machine Intelligence Research Labs, Auburn, WA, USA Abstract: Prediction of poly(lactic-co-glycolic acid) (PLGA) micro- and nanoparticles’ dissolution rates plays a significant role in pharmaceutical and medical industries. The prediction of PLGA dissolution rate is crucial for drug manufacturing. Therefore, a model that predicts the PLGA dissolution rate could be beneficial. PLGA dissolution is influenced by numerous factors (features), and counting the known features leads to a dataset with 300 features. This large number of features and high redundancy within the dataset makes the prediction task very difficult and inaccurate. In this study, dimensionality reduction techniques were applied in order to simplify the task and eliminate irrelevant and redundant features. A heterogeneous pool of several regression algorithms were independently tested and evaluated. In addition, several ensemble methods were tested in order to improve the accuracy of prediction. The empirical results revealed that the proposed evolutionary weighted ensemble method offered the lowest margin of error and significantly outperformed the individual algorithms and the other ensemble techniques. Keywords: feature selection, regression models, ensemble, protein dissolutionOjha VKJackowski KAbraham ASnášel VDove Medical PressarticleMedicine (General)R5-920ENInternational Journal of Nanomedicine, Vol 2015, Iss default, Pp 1119-1129 (2015)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine (General) R5-920
spellingShingle	Medicine (General) R5-920 Ojha VK Jackowski K Abraham A Snášel V Dimensionality reduction, and function approximation of poly(lactic-co-glycolic acid) micro- and nanoparticle dissolution rate
description	Varun Kumar Ojha,1,2 Konrad Jackowski,3 Ajith Abraham,1,4 Václav Sná&scaron;el1,2 1IT4Innovations, V&Scaron;B – Technical University of Ostrava, Ostrava, Czech Republic; 2Department of Computer Science, V&Scaron;B – Technical University of Ostrava, Ostrava, Czech Republic; 3Department of Systems and Computer Networks, Wroclaw University of Technology, Wroclaw, Poland; 4Machine Intelligence Research Labs, Auburn, WA, USA Abstract: Prediction of poly(lactic-co-glycolic acid) (PLGA) micro- and nanoparticles’ dissolution rates plays a significant role in pharmaceutical and medical industries. The prediction of PLGA dissolution rate is crucial for drug manufacturing. Therefore, a model that predicts the PLGA dissolution rate could be beneficial. PLGA dissolution is influenced by numerous factors (features), and counting the known features leads to a dataset with 300 features. This large number of features and high redundancy within the dataset makes the prediction task very difficult and inaccurate. In this study, dimensionality reduction techniques were applied in order to simplify the task and eliminate irrelevant and redundant features. A heterogeneous pool of several regression algorithms were independently tested and evaluated. In addition, several ensemble methods were tested in order to improve the accuracy of prediction. The empirical results revealed that the proposed evolutionary weighted ensemble method offered the lowest margin of error and significantly outperformed the individual algorithms and the other ensemble techniques. Keywords: feature selection, regression models, ensemble, protein dissolution
format	article
author	Ojha VK Jackowski K Abraham A Snášel V
author_facet	Ojha VK Jackowski K Abraham A Snášel V
author_sort	Ojha VK
title	Dimensionality reduction, and function approximation of poly(lactic-co-glycolic acid) micro- and nanoparticle dissolution rate
title_short	Dimensionality reduction, and function approximation of poly(lactic-co-glycolic acid) micro- and nanoparticle dissolution rate
title_full	Dimensionality reduction, and function approximation of poly(lactic-co-glycolic acid) micro- and nanoparticle dissolution rate
title_fullStr	Dimensionality reduction, and function approximation of poly(lactic-co-glycolic acid) micro- and nanoparticle dissolution rate
title_full_unstemmed	Dimensionality reduction, and function approximation of poly(lactic-co-glycolic acid) micro- and nanoparticle dissolution rate
title_sort	dimensionality reduction, and function approximation of poly(lactic-co-glycolic acid) micro- and nanoparticle dissolution rate
publisher	Dove Medical Press
publishDate	2015
url	https://doaj.org/article/a9627258154f4b7aaadf7a3aab9a6663
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_version_	1718400551170867200

Dimensionality reduction, and function approximation of poly(lactic-co-glycolic acid) micro- and nanoparticle dissolution rate

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