Integrated Application of Enhanced Replacement Method and Ensemble Learning for the Prediction of BCRP/ABCG2 Substrates

MELISA E. GANTNER; LUCAS N. ALBERCA; ANDREW MERCADER; LUIS E. BRUNO-BLANCH; ALAN TALEVI

CURRENT BIOINFORMATICS

BENTHAM SCIENCE PUBL LTD

Lugar: Oak Park; Año: 2017 vol. 12 p. 239 - 248

1574-8936

Breast 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.