A nonparametric weighted feature extraction-based method for c-Jun N-terminal kinase-3 inhibitor prediction.

Affiliation

Department of Computing and Numerical Analysis, University of Córdoba, Campus de Rabanales, Albert Einstein Building, E-14071, Córdoba, Spain. Electronic address: [Email]

Abstract

In this work, the application of a new strategy called NWFE ensemble (nonparametric weighted feature extraction ensemble) method is proposed. Subspace-supervised projections based on NWFE are incorporated into the construction of ensembles of classifiers to facilitate the correct classification of wrongly classified instances without being detrimental to the overall performance of the ensemble. The performance of NWFE is investigated with a c-Jun N-terminal kinase-3 inhibitor benchmark dataset using different chemical compound representation models. Compared with the standard method, the results obtained show that the applied method improves the prediction performance using two classifiers based on decision trees and support vector machines.

Keywords

Classifier ensembles,Molecular activity predictions,Nonparametric weighted feature extraction,Supervised projections,