ArticleIEEE/ACM transactions on computational biology and bioinformatics
Prediction of R5, X4, and R5X4 HIV-1 coreceptor usage with evolved neural networks.
Article in IEEE/ACM transactions on computational biology and bioinformatics. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed, 43 citations in OpenAlex.
- HIV-1 subtypes maintain distinctive physicochemical signatures in Nef domains associated with immunoregulation.Infection, genetics and evolution : journal of molecular epidemiology and evolutionary genetics in infectious diseases · 2023Article
- Using machine learning methods to determine a typology of patients with HIV-HCV infection to be treated with antivirals.PloS one · 2020Observational
- Brain-specific HIV Nef identified in multiple patients with neurological disease.Journal of neurovirology · 2018Article
- Eradication of HIV from Tissue Reservoirs: Challenges for the Cure.AIDS research and human retroviruses · 2018Review
- On the Physicochemical and Structural Modifications Associated with HIV-1 Subtype B Tropism Transition.AIDS research and human retroviruses · 2016Article
- Identification of dual-tropic HIV-1 using evolved neural networks.Bio Systems · 2015Article
- HIV-associated neuropathogenesis: a systems biology perspective for modeling and therapy.Bio Systems · 2014Review
- Hybrid approach for predicting coreceptor used by HIV-1 from its V3 loop amino acid sequence.PloS one · 2013Article
- Clinical significance of HIV-1 coreceptor usage.Journal of translational medicine · 2011Review
- HIV-1 nef protein visits B-cells via macrophage nanotubes: a mechanism for AIDS-related lymphoma pathogenesis?Current HIV research · 2010Article
- Comparative determination of HIV-1 co-receptor tropism by Enhanced Sensitivity Trofile, gp120 V3-loop RNA and DNA genotyping.Retrovirology · 2010Article
- Human immunodeficiency virus-1 evolutionary patterns associated with pathogenic processes in the brain.Journal of neurovirology · 2010Article
- Prediction of co-receptor usage of HIV-1 from genotype.PLoS computational biology · 2010Article
- How HIV changes its tropism: evolution and adaptation?Current opinion in HIV and AIDS · 2009Review
- HIV-1 coreceptor usage prediction without multiple alignments: an application of string kernels.Retrovirology · 2008Article
Corrections and comments
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Authors and funding
4 authors at 4 institutions in 2 countries.
Funding
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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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.