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Current Bioinformatics


ISSN (Print): 1574-8936
ISSN (Online): 2212-392X

Research Article

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

Author(s): Melisa E. Gantner, Lucas N. Alberca, Andrew G. Mercader, Luis E. Bruno-Blanch and Alan Talevia*

Volume 12, Issue 3, 2017

Page: [239 - 248] Pages: 10

DOI: 10.2174/1574893611666151109193016

Price: $65


Background: Breast Cancer Resistance Protein (BCRP or ABCG2) is a polyspecific effluxtransporter 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. Therefore, the early recognition of BCRP substrates seems to be crucial in the early phase of drug discovery.

Objective: The development of computational models that allow the early detection of BCRP substrates and non-substrates.

Method: 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 nonsubstrates of the wild type human BCRP.

Results: The ensemble learning approach combining the 10-Enhanced Replacement Method best individual models obtained through MAX Operator displayed the best ability to discriminate between BCRP substrates and non-substrates across all the validation sets/libraries used.

Conclusion: 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 application of the Enhanced Replacement Method to solve classification problems.

Keywords: Breast Cancer Resistance Protein, ABC transporters, ABCG2, enhanced replacement method, ensemble learning, linear classifiers.

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