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

Paul D. Yoo*, Yung Shwen Ho, Jason Ng, Michael Charleston, Nitin K. Saksena, Pengyi Yang, Albert Y. Zomaya

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

6 Scopus citations


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.

Original languageEnglish (US)
Article numberS22
JournalBMC genomics
Issue numberSUPPL. 4
StatePublished - Dec 2 2010
Externally publishedYes

Bibliographical note

Funding Information:
This work is supported by KUSTAR, and Centres for Distributed and High Performance Computing, Mathematical Biology & Sydney Bioinformatics, University of Sydney, Australia. This article has been published as part of BMC Genomics Volume 11 Supplement 4, 2010: Ninth International Conference on Bioinformatics (InCoB2010): Computational Biology. The full contents of the supplement are available online at http://www.biomedcentral.com/1471-2164/11?issue=S4.

ASJC Scopus subject areas

  • Biotechnology
  • Genetics


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