Prediction of human immunodeficiency virus drug resistance using contact energies

Isis Bonet Cruz, Maria Matilde García Lorenzo, Ricardo Grau Ábalo, Robersy Sánchez Rodríguez

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

The HIV-1 protease drug susceptibility data sets from the Stanford HIV-1 drug resistance database were utilized to determine drug susceptibility to seven protease inhibitors using viral genotype. Using the drug-specific resistance-fold values associated with each sample, the dataset of phenotypes were grouped into two classes. The contact energies where used to represent the protease sequence of HIV. Two methods were use to predict de drug resistance: Multi Layer Perceptron (MLP) and Support Vector Machine (SMV). SVMs were use with different types of kernel function. Both MLP and SVM were compared with previously published classification models. We found prediction percent between 80-92.3 for MLP and prediction percent between 75.2-91.8 for SVM.

Original languageEnglish (US)
Title of host publicationProceedings of 2005 International Conference on Neural Networks and Brain Proceedings, ICNNB'05
Pages490-493
Number of pages4
StatePublished - Dec 1 2005
Event2005 International Conference on Neural Networks and Brain Proceedings, ICNNB'05 - Beijing, China
Duration: Oct 13 2005Oct 15 2005

Publication series

NameProceedings of 2005 International Conference on Neural Networks and Brain Proceedings, ICNNB'05
Volume1

Other

Other2005 International Conference on Neural Networks and Brain Proceedings, ICNNB'05
CountryChina
CityBeijing
Period10/13/0510/15/05

All Science Journal Classification (ASJC) codes

  • Engineering(all)

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  • Cite this

    Cruz, I. B., Lorenzo, M. M. G., Ábalo, R. G., & Rodríguez, R. S. (2005). Prediction of human immunodeficiency virus drug resistance using contact energies. In Proceedings of 2005 International Conference on Neural Networks and Brain Proceedings, ICNNB'05 (pp. 490-493). [1614660] (Proceedings of 2005 International Conference on Neural Networks and Brain Proceedings, ICNNB'05; Vol. 1).