title: Lopinavir Resistance Classification with Imbalanced Data Using Probabilistic Neural Networks.
abstract: Resistance to antiretroviral drugs has been a major obstacle for long-lasting treatment of HIV-infected patients. The development of models to predict drug resistance is recognized as useful for helping the decision of the best therapy for each HIV+ individual. The aim of this study was to develop classifiers for predicting resistance to the HIV protease inhibitor lopinavir using a probabilistic neural network (PNN). The data were provided by the Molecular Virology Laboratory of the Health Sciences Center, Federal University of Rio de Janeiro (CCS-UFRJ/Brazil). Using bootstrap and stepwise techniques, ten features were selected by logistic regression (LR) to be used as inputs to the network. Bootstrap and cross-validation were used to define the smoothing parameter of the PNN networks. Four balanced models were designed and evaluated using a separate test set. The accuracies of the classifiers with the test set ranged from 0.89 to 0.94, and the area under the receiver operating characteristic (ROC) curve (AUC) ranged from 0.96 to 0.97. The sensitivity ranged from 0.94 to 1.00, and the specificity was between 0.88 and 0.92. Four classifiers showed performances very close to three existing expert-based interpretation systems, the HIVdb, the Rega and the ANRS algorithms, and to a k-Nearest Neighbor.
authors.affiliation: Biomedical Engineering Program, Federal University of Rio de Janeiro - UFRJ, Ilha do Fundão, Rio de Janeiro, RJ, Brazil. raposo@peb.ufrj.br.
authors.forename: Letícia M
authors.initials: LM
authors.lastname: Raposo
authors.affiliation: Laboratory of Molecular Virology, Federal University of Rio de Janeiro - UFRJ, Ilha do Fundão, Rio de Janeiro, RJ, Brazil.
authors.forename: Mônica B
authors.initials: MB
authors.lastname: Arruda
authors.affiliation: Laboratory of Molecular Virology, Federal University of Rio de Janeiro - UFRJ, Ilha do Fundão, Rio de Janeiro, RJ, Brazil.
authors.forename: Rodrigo M
authors.initials: RM
authors.lastname: de Brindeiro
authors.affiliation: Biomedical Engineering Program, Federal University of Rio de Janeiro - UFRJ, Ilha do Fundão, Rio de Janeiro, RJ, Brazil.
authors.forename: Flavio F
authors.initials: FF
authors.lastname: Nobre
mesh_terms: D000465:Algorithms; D000998:Antiviral Agents / Q000494:pharmacology* / Q000627:therapeutic use; D024882:Drug Resistance, Viral; D015658:HIV Infections / Q000188:drug therapy; D006801:Humans; D016015:Logistic Models; D061466:Lopinavir / Q000494:pharmacology* / Q000627:therapeutic use; D016571:Neural Networks, Computer; D011237:Predictive Value of Tests; D012985:Software Design
chemical_list: D000998:Antiviral Agents; D061466:Lopinavir
keywords: Classifier; HIV; Probabilistic neural networks; Resistance