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Hierarchical kernel mixture models for the prediction of AIDS disease progression using HIV structural gp120 profiles

Changes to the glycosylation profile on HIV gp120 can influence viral pathogenesis and alter AIDS disease progression. The characterization of glycosylation differences at the sequence level is inadequate as the placement of carbohydrates is structurally complex. However, no structural framework is...

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Autores principales: Yoo, Paul D, Shwen Ho, Yung, Ng, Jason, Charleston, Michael, Saksena, Nitin K, Yang, Pengyi, Zomaya, Albert Y
Formato: Texto
Lenguaje:English
Publicado: BioMed Central 2010
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3005921/
https://www.ncbi.nlm.nih.gov/pubmed/21143806
http://dx.doi.org/10.1186/1471-2164-11-S4-S22
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author Yoo, Paul D
Shwen Ho, Yung
Ng, Jason
Charleston, Michael
Saksena, Nitin K
Yang, Pengyi
Zomaya, Albert Y
author_facet Yoo, Paul D
Shwen Ho, Yung
Ng, Jason
Charleston, Michael
Saksena, Nitin K
Yang, Pengyi
Zomaya, Albert Y
author_sort Yoo, Paul D
collection PubMed
description Changes to the glycosylation profile on HIV gp120 can influence viral pathogenesis and alter AIDS disease progression. The characterization of glycosylation differences at the sequence level is inadequate as the placement of carbohydrates is structurally complex. However, no structural framework is available to date for the study of HIV disease progression. In this study, we propose a novel machine-learning based framework for the prediction of AIDS disease progression in three stages (RP, SP, and LTNP) using the HIV structural gp120 profile. This new intelligent framework proves to be accurate and provides an important benchmark for predicting AIDS disease progression computationally. The model is trained using a novel HIV gp120 glycosylation structural profile to detect possible stages of AIDS disease progression for the target sequences of HIV(+) individuals. The performance of the proposed model was compared to seven existing different machine-learning models on newly proposed gp120-Benchmark_1 dataset in terms of error-rate (MSE), accuracy (CCI), stability (STD), and complexity (TBM). The novel framework showed better predictive performance with 67.82% CCI, 30.21 MSE, 0.8 STD, and 2.62 TBM on the three stages of AIDS disease progression of 50 HIV+ individuals. This framework is an invaluable bioinformatics tool that will be useful to the clinical assessment of viral pathogenesis.
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spelling pubmed-30059212010-12-22 Hierarchical kernel mixture models for the prediction of AIDS disease progression using HIV structural gp120 profiles Yoo, Paul D Shwen Ho, Yung Ng, Jason Charleston, Michael Saksena, Nitin K Yang, Pengyi Zomaya, Albert Y BMC Genomics Proceedings Changes to the glycosylation profile on HIV gp120 can influence viral pathogenesis and alter AIDS disease progression. The characterization of glycosylation differences at the sequence level is inadequate as the placement of carbohydrates is structurally complex. However, no structural framework is available to date for the study of HIV disease progression. In this study, we propose a novel machine-learning based framework for the prediction of AIDS disease progression in three stages (RP, SP, and LTNP) using the HIV structural gp120 profile. This new intelligent framework proves to be accurate and provides an important benchmark for predicting AIDS disease progression computationally. The model is trained using a novel HIV gp120 glycosylation structural profile to detect possible stages of AIDS disease progression for the target sequences of HIV(+) individuals. The performance of the proposed model was compared to seven existing different machine-learning models on newly proposed gp120-Benchmark_1 dataset in terms of error-rate (MSE), accuracy (CCI), stability (STD), and complexity (TBM). The novel framework showed better predictive performance with 67.82% CCI, 30.21 MSE, 0.8 STD, and 2.62 TBM on the three stages of AIDS disease progression of 50 HIV+ individuals. This framework is an invaluable bioinformatics tool that will be useful to the clinical assessment of viral pathogenesis. BioMed Central 2010-12-02 /pmc/articles/PMC3005921/ /pubmed/21143806 http://dx.doi.org/10.1186/1471-2164-11-S4-S22 Text en Copyright ©2010 Yoo et al; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Proceedings
Yoo, Paul D
Shwen Ho, Yung
Ng, Jason
Charleston, Michael
Saksena, Nitin K
Yang, Pengyi
Zomaya, Albert Y
Hierarchical kernel mixture models for the prediction of AIDS disease progression using HIV structural gp120 profiles
title Hierarchical kernel mixture models for the prediction of AIDS disease progression using HIV structural gp120 profiles
title_full Hierarchical kernel mixture models for the prediction of AIDS disease progression using HIV structural gp120 profiles
title_fullStr Hierarchical kernel mixture models for the prediction of AIDS disease progression using HIV structural gp120 profiles
title_full_unstemmed Hierarchical kernel mixture models for the prediction of AIDS disease progression using HIV structural gp120 profiles
title_short Hierarchical kernel mixture models for the prediction of AIDS disease progression using HIV structural gp120 profiles
title_sort hierarchical kernel mixture models for the prediction of aids disease progression using hiv structural gp120 profiles
topic Proceedings
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3005921/
https://www.ncbi.nlm.nih.gov/pubmed/21143806
http://dx.doi.org/10.1186/1471-2164-11-S4-S22
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