Predicting drug resistance related to ABC transporters using unsupervised Consensus Self-Organizing Maps

Abstract ATP binding cassette (ABC) transporters play a pivotal role in drug elimination, particularly on several types of cancer in which these proteins are overexpressed. Due to their promiscuous ligand recognition, building computational models for substrate classification is quite challenging. T...

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Autores principales:	Roger Estrada-Tejedor, Gerhard F. Ecker
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2018
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/1af7f668c79d46b3b66dc433b7fba566
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spelling	oai:doaj.org-article:1af7f668c79d46b3b66dc433b7fba5662021-12-02T11:40:46ZPredicting drug resistance related to ABC transporters using unsupervised Consensus Self-Organizing Maps10.1038/s41598-018-25235-92045-2322https://doaj.org/article/1af7f668c79d46b3b66dc433b7fba5662018-05-01T00:00:00Zhttps://doi.org/10.1038/s41598-018-25235-9https://doaj.org/toc/2045-2322Abstract ATP binding cassette (ABC) transporters play a pivotal role in drug elimination, particularly on several types of cancer in which these proteins are overexpressed. Due to their promiscuous ligand recognition, building computational models for substrate classification is quite challenging. This study evaluates the use of modified Self-Organizing Maps (SOM) for predicting drug resistance associated with P-gp, MPR1 and BCRP activity. Herein, we present a novel multi-labelled unsupervised classification model which combines a new clustering algorithm with SOM. It significantly improves the accuracy of substrates classification, catching up with traditional supervised machine learning algorithms. Results can be applied to predict the pharmacological profile of new drug candidates during the drug development process.Roger Estrada-TejedorGerhard F. EckerNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 8, Iss 1, Pp 1-9 (2018)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Roger Estrada-Tejedor Gerhard F. Ecker Predicting drug resistance related to ABC transporters using unsupervised Consensus Self-Organizing Maps
description	Abstract ATP binding cassette (ABC) transporters play a pivotal role in drug elimination, particularly on several types of cancer in which these proteins are overexpressed. Due to their promiscuous ligand recognition, building computational models for substrate classification is quite challenging. This study evaluates the use of modified Self-Organizing Maps (SOM) for predicting drug resistance associated with P-gp, MPR1 and BCRP activity. Herein, we present a novel multi-labelled unsupervised classification model which combines a new clustering algorithm with SOM. It significantly improves the accuracy of substrates classification, catching up with traditional supervised machine learning algorithms. Results can be applied to predict the pharmacological profile of new drug candidates during the drug development process.
format	article
author	Roger Estrada-Tejedor Gerhard F. Ecker
author_facet	Roger Estrada-Tejedor Gerhard F. Ecker
author_sort	Roger Estrada-Tejedor
title	Predicting drug resistance related to ABC transporters using unsupervised Consensus Self-Organizing Maps
title_short	Predicting drug resistance related to ABC transporters using unsupervised Consensus Self-Organizing Maps
title_full	Predicting drug resistance related to ABC transporters using unsupervised Consensus Self-Organizing Maps
title_fullStr	Predicting drug resistance related to ABC transporters using unsupervised Consensus Self-Organizing Maps
title_full_unstemmed	Predicting drug resistance related to ABC transporters using unsupervised Consensus Self-Organizing Maps
title_sort	predicting drug resistance related to abc transporters using unsupervised consensus self-organizing maps
publisher	Nature Portfolio
publishDate	2018
url	https://doaj.org/article/1af7f668c79d46b3b66dc433b7fba566
work_keys_str_mv	AT rogerestradatejedor predictingdrugresistancerelatedtoabctransportersusingunsupervisedconsensusselforganizingmaps AT gerhardfecker predictingdrugresistancerelatedtoabctransportersusingunsupervisedconsensusselforganizingmaps
_version_	1718395505914937344

Predicting drug resistance related to ABC transporters using unsupervised Consensus Self-Organizing Maps

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