dc.creatorGantner, Melisa Edith
dc.creatorAlberca, Lucas Nicolás
dc.creatorMercader, Andrew Gustavo
dc.creatorBruno Blanch, Luis Enrique
dc.creatorTalevi, Alan
dc.date2017
dc.date2021-05-04T18:29:49Z
dc.date.accessioned2023-07-15T01:36:28Z
dc.date.available2023-07-15T01:36:28Z
dc.identifierhttp://sedici.unlp.edu.ar/handle/10915/118321
dc.identifierissn:1574-8936
dc.identifier.urihttps://repositorioslatinoamericanos.uchile.cl/handle/2250/7458893
dc.descriptionBreast Cancer Resistance Protein (BCRP or ABCG2) is a polyspecific efflux-transporter which belongs to the ATP-binding Cassette superfamily. Up-regulation of BCRP is associated to multi-drug resistance in a number of conditions, e.g. cancer and epilepsy. Recent proteomic studies show that high-expression levels of BCRP are found in healthy human intestine and at the blood-brain barrier, limiting the absorption and brain distribution of its substrates. Here, we have jointly applied the Enhanced Replacement Method and ensemble learning approaches to obtain combinations of 2D linear classifiers capable of discriminating among substrates and non-substrates of the wild type human BCRP. The best model ensemble obtained outperforms previously reported 2D linear classifiers, showing the ability of the Enhanced Replacement Method and ensemble learning schemes to optimize the performance of individual models. This is the first report of the Enhanced Replacement Method to solve classification problems.
dc.descriptionFacultad de Ciencias Exactas
dc.formatapplication/pdf
dc.languageen
dc.rightshttp://creativecommons.org/licenses/by-nc-sa/4.0/
dc.rightsCreative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)
dc.subjectQuímica
dc.subjectBreast Cancer Resistance Protein
dc.subjectABC Transporters
dc.subjectABCG2
dc.subjectEnhanced Replacement Method
dc.subjectEnsemble Learning
dc.subjectLinear Classifiers
dc.titleIntegrated Application of Enhanced Replacement Method and Ensemble Learning for the Prediction of BCRP/ABCG2 Substrates
dc.typeArticulo
dc.typePreprint


Este ítem pertenece a la siguiente institución