Lopinavir Resistance Classification with Imbalanced Data Using Probabilistic Neural Networks.
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 c...
| Publicado en: | Journal of Medical Systems Vol. 40; no. 3; pp. 1 - 8 |
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| Autores principales: | , , , |
| Formato: | equations & formulas pictorial research Journal Article |
| Publicado: |
Springer Nature
Mar2016
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=115925256&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 115925256 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01485598 4N0 jtl: Journal of Medical Systems issn: 01485598 maglogo: N pubinfo: dt: Mar2016 vid: 40 iid: 3 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 115925256 115925256 115925256 10.1007/s10916-015-0428-7 115925256 ppf: 1 ppct: 7 formats: fmt: @attributes: type: P tig: atl: Lopinavir Resistance Classification with Imbalanced Data Using Probabilistic Neural Networks. aug: au: Raposo, Letícia Arruda, Mônica Brindeiro, Rodrigo Nobre, Flavio affil: Biomedical Engineering Program, Federal University of Rio de Janeiro - UFRJ, Ilha do Fundão, Rio de Janeiro Brazil sug: subj: HIV Infections Drug Therapy Anti-Retroviral Agents Therapeutic Use Lopinavir Ritonavir Drug Resistance Evaluation Neural Networks (Computer) Utilization Probability Logistic Regression ROC Curve Sensitivity and Specificity Genotype Human Confidence Intervals Data Analysis Software Algorithms Funding Source ab: 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. pubtype: Academic Journal doctype: equations & formulas pictorial research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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