article

Prediction of R5, X4, and R5X4 HIV-1 Coreceptor Usage with Evolved Neural Networks

Authors:

Susanna L. Lamers,

Michael S. McGrath,

Gary B. FogelAuthors Info & Claims

IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB), Volume 5, Issue 2

Pages 291 - 300

https://doi.org/10.1109/TCBB.2007.1074

Published: 01 April 2008 Publication History

Abstract

The HIV-1 genome is highly heterogeneous. This variation affords the virus a wide range of molecular properties, including the ability to infect cell types, such as macrophages and lymphocytes, expressing different chemokine receptors on the cell surface. In particular, R5 HIV-1 viruses use CCR5 as co-receptor for viral entry, X4 viruses use CXCR4, whereas some viral strains, known as R5X4 or D-tropic, have the ability to utilize both co-receptors. X4 and R5X4 viruses are associated with rapid disease progression to AIDS. R5X4 viruses differ in that they have yet to be characterized by the examination of the genetic sequence of HIV-1 alone. In this study, a series of experiments was performed to evaluate different strategies of feature selection and neural network optimization. We demonstrate the use of artificial neural networks trained via evolutionary computation to predict viral co-receptor usage. The results indicate identification of R5X4 viruses with predictive accuracy of 75.5%.

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Cited By

Fogel GLiu EAbramson BPetrus SBarrus TSutton GMichael TThomas S(2022)Identification of Synthetic Engineering in Prokaryotic Genomes Using Evolved Neural Networks2022 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB)10.1109/CIBCB55180.2022.9863024(1-8)Online publication date: 15-Aug-2022
https://dl.acm.org/doi/10.1109/CIBCB55180.2022.9863024
Liu EFogel GNolan DLamers SMcGrath M(2021)Using Evolved Neural Networks to Elucidate Nef Features Associated with HIV-1 Subtype Differentiation2021 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB)10.1109/CIBCB49929.2021.9562798(1-8)Online publication date: 13-Oct-2021
https://dl.acm.org/doi/10.1109/CIBCB49929.2021.9562798
Liu EFogel GNolan DLamers SMcGrath M(2020)Using Neural Networks to Identify Features Associated with HIV Nef Protein and Cancer2020 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB)10.1109/CIBCB48159.2020.9277717(1-8)Online publication date: 27-Oct-2020
https://dl.acm.org/doi/10.1109/CIBCB48159.2020.9277717

Index Terms

Prediction of R5, X4, and R5X4 HIV-1 Coreceptor Usage with Evolved Neural Networks
1. Applied computing
  1. Life and medical sciences
2. Computing methodologies

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cover image IEEE/ACM Transactions on Computational Biology and Bioinformatics

IEEE/ACM Transactions on Computational Biology and Bioinformatics Volume 5, Issue 2

April 2008

158 pages

ISSN:1545-5963

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IEEE Computer Society Press

Washington, DC, United States

Publication History

Published: 01 April 2008

Published in TCBB Volume 5, Issue 2

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Cited By

Fogel GLiu EAbramson BPetrus SBarrus TSutton GMichael TThomas S(2022)Identification of Synthetic Engineering in Prokaryotic Genomes Using Evolved Neural Networks2022 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB)10.1109/CIBCB55180.2022.9863024(1-8)Online publication date: 15-Aug-2022
https://dl.acm.org/doi/10.1109/CIBCB55180.2022.9863024
Liu EFogel GNolan DLamers SMcGrath M(2021)Using Evolved Neural Networks to Elucidate Nef Features Associated with HIV-1 Subtype Differentiation2021 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB)10.1109/CIBCB49929.2021.9562798(1-8)Online publication date: 13-Oct-2021
https://dl.acm.org/doi/10.1109/CIBCB49929.2021.9562798
Liu EFogel GNolan DLamers SMcGrath M(2020)Using Neural Networks to Identify Features Associated with HIV Nef Protein and Cancer2020 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB)10.1109/CIBCB48159.2020.9277717(1-8)Online publication date: 27-Oct-2020
https://dl.acm.org/doi/10.1109/CIBCB48159.2020.9277717

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