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BioMed Research International
Volume 2013 (2013), Article ID 579741, 6 pages
Potential Impact of a Free Online HIV Treatment Response Prediction System for Reducing Virological Failures and Drug Costs after Antiretroviral Therapy Failure in a Resource-Limited Setting
1The HIV Resistance Response Database Initiative (RDI), 14 Union Square, London N1 7DH, UK
2Rural Development Trust (RDT) Hospital, Bathalapalli, 515661 AP, India
3Chelsea and Westminster Hospital, London SW10 9NH, UK
4BC Centre for Excellence in HIV/AIDS, Vancouver, Canada
5National Institute of Allergy and Infectious Diseases, Bethesda, MD 20892, USA
Received 22 April 2013; Accepted 9 July 2013
Academic Editor: Marcelo A. Soares
Copyright © 2013 Andrew D. Revell et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Citations to this Article [3 citations]
The following is the list of published articles that have cited the current article.
- Ad Revell, Ma Boyd, D Wang, S Emery, B Gazzard, P Reiss, Ai van Sighem, Js Montaner, Hc Lane, and Ba Larder, “A comparison of computational models with and without genotyping for prediction of response to second-line HIV therapy,” HIV Medicine, 2014.
- Andrew Revell, Paul Khabo, Lotty Ledwaba, Sean Emery, Dechao Wang, Robin Wood, Carl Morrow, Hugo Tempelman, Raph L. Hamers, Peter Reiss, Ard van Sighem, Anton Pozniak, Julio Montaner, H. Clifford Lane, and Brendan Larder, “Computational models as predictors of HIV treatment outcomes for the Phidisa cohort in South Africa,” Southern African Journal of HIV Medicine, vol. 17, no. 1, 2016.
- Maurizio Zazzi, Alessandro Cozzi-Lepri, and Mattia C. F. Prosperi, “Computer-Aided Optimization of Combined Anti-Retroviral Therapy for HIV: New Drugs, New Drug Targets and Drug Resistance,” Current Hiv Research, vol. 14, no. 2, pp. 101–109, 2016.