Prediction of human immunodeficiency virus drug resistance using contact energies

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

Prediction of human immunodeficiency virus drug resistance using contact energies

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

Biology

Research output: Chapter in Book/Report/Conference proceeding › Conference 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 language	English (US)
Title of host publication	Proceedings of 2005 International Conference on Neural Networks and Brain Proceedings, ICNNB'05
Pages	490-493
Number of pages	4
State	Published - 2005
Event	2005 International Conference on Neural Networks and Brain Proceedings, ICNNB'05 - Beijing, China Duration: Oct 13 2005 → Oct 15 2005

Publication series

Name	Proceedings of 2005 International Conference on Neural Networks and Brain Proceedings, ICNNB'05
Volume	1

Other

Other	2005 International Conference on Neural Networks and Brain Proceedings, ICNNB'05
Country/Territory	China
City	Beijing
Period	10/13/05 → 10/15/05

All Science Journal Classification (ASJC) codes

General Engineering

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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). Article 1614660 (Proceedings of 2005 International Conference on Neural Networks and Brain Proceedings, ICNNB'05; Vol. 1).

Cruz, Isis Bonet ; Lorenzo, Maria Matilde García ; Ábalo, Ricardo Grau et al. / Prediction of human immunodeficiency virus drug resistance using contact energies. Proceedings of 2005 International Conference on Neural Networks and Brain Proceedings, ICNNB'05. 2005. pp. 490-493 (Proceedings of 2005 International Conference on Neural Networks and Brain Proceedings, ICNNB'05).

@inproceedings{e8274558ce9445af809a752ab03553f0,

title = "Prediction of human immunodeficiency virus drug resistance using contact energies",

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.",

author = "Cruz, {Isis Bonet} and Lorenzo, {Maria Matilde Garc{\'i}a} and {\'A}balo, {Ricardo Grau} and Rodr{\'i}guez, {Robersy S{\'a}nchez}",

note = "Copyright: Copyright 2008 Elsevier B.V., All rights reserved.; 2005 International Conference on Neural Networks and Brain Proceedings, ICNNB'05 ; Conference date: 13-10-2005 Through 15-10-2005",

year = "2005",

language = "English (US)",

isbn = "0780394224",

series = "Proceedings of 2005 International Conference on Neural Networks and Brain Proceedings, ICNNB'05",

pages = "490--493",

booktitle = "Proceedings of 2005 International Conference on Neural Networks and Brain Proceedings, ICNNB'05",

}

Cruz, IB, Lorenzo, MMG, Ábalo, RG & Rodríguez, RS 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., 1614660, Proceedings of 2005 International Conference on Neural Networks and Brain Proceedings, ICNNB'05, vol. 1, pp. 490-493, 2005 International Conference on Neural Networks and Brain Proceedings, ICNNB'05, Beijing, China, 10/13/05.

Prediction of human immunodeficiency virus drug resistance using contact energies. / Cruz, Isis Bonet; Lorenzo, Maria Matilde García; Ábalo, Ricardo Grau et al.
Proceedings of 2005 International Conference on Neural Networks and Brain Proceedings, ICNNB'05. 2005. p. 490-493 1614660 (Proceedings of 2005 International Conference on Neural Networks and Brain Proceedings, ICNNB'05; Vol. 1).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - Prediction of human immunodeficiency virus drug resistance using contact energies

AU - Cruz, Isis Bonet

AU - Lorenzo, Maria Matilde García

AU - Ábalo, Ricardo Grau

AU - Rodríguez, Robersy Sánchez

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N2 - 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.

AB - 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.

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M3 - Conference contribution

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SN - 9780780394223

T3 - Proceedings of 2005 International Conference on Neural Networks and Brain Proceedings, ICNNB'05

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BT - Proceedings of 2005 International Conference on Neural Networks and Brain Proceedings, ICNNB'05

T2 - 2005 International Conference on Neural Networks and Brain Proceedings, ICNNB'05

Y2 - 13 October 2005 through 15 October 2005

ER -

Prediction of human immunodeficiency virus drug resistance using contact energies

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