Computational models as predictors of HIV treatment outcomes for the Phidisa cohort in South Africa

BACKGROUND: Selecting the optimal combination of HIV drugs for an individual in resource-limited settings is challenging because of the limited availability of drugs and genotyping. OBJECTIVE: The evaluation as a potential treatment support tool of computational models that predict response to thera...

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Detalles Bibliográficos
Autores principales: Revell, Andrew, Khabo, Paul, Ledwaba, Lotty, Emery, Sean, Wang, Dechao, Wood, Robin, Morrow, Carl, Tempelman, Hugo, Hamers, Raph L., Reiss, Peter, van Sighem, Ard, Pozniak, Anton, Montaner, Julio, Lane, H. Clifford, Larder, Brendan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: AOSIS 2016
Materias:
Original Research
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5843195/
https://www.ncbi.nlm.nih.gov/pubmed/29568609
http://dx.doi.org/10.4102/sajhivmed.v17i1.450

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5843195/
https://www.ncbi.nlm.nih.gov/pubmed/29568609
http://dx.doi.org/10.4102/sajhivmed.v17i1.450

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