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HandWiki. Resistance Database Initiative. Encyclopedia. Available online: https://encyclopedia.pub/entry/32980 (accessed on 11 September 2026).
HandWiki. Resistance Database Initiative. Encyclopedia. Available at: https://encyclopedia.pub/entry/32980. Accessed September 11, 2026.
HandWiki. "Resistance Database Initiative" Encyclopedia, https://encyclopedia.pub/entry/32980 (accessed September 11, 2026).
HandWiki. (2022, November 04). Resistance Database Initiative. In Encyclopedia. https://encyclopedia.pub/entry/32980
HandWiki. "Resistance Database Initiative." Encyclopedia. Web. 04 November, 2022.
Resistance Database Initiative
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The content is sourced from: https://handwiki.org/wiki/Resistance_Database_Initiative

HIV Resistance Response Database Initiative (RDI) is a not-for-profit organisation established in 2002 with the mission of improving the clinical management of HIV infection through the application of bioinformatics to HIV drug resistance and treatment outcome data. The RDI has the following specific goals: The RDI consists of a small executive group based in the UK, an international advisory group of leading HIV/AIDS scientists and clinicians, and an extensive global network of collaborators and data contributors.

clinical management drug resistance hiv

References

  1. Wang, Dechao (2009). "A comparison of three computational modelling methods for the prediction of virological response to combination HIV therapy". Artificial Intelligence in Medicine 47 (1): 6374. doi:10.1016/j.artmed.2009.05.002.  https://dx.doi.org/10.1016%2Fj.artmed.2009.05.002
  2. Larder, Brendan (2007). "The development of artificial neural networks to predict virological response to combination HIV therapy". Antiviral Therapy 12 (12): 15–24. 
  3. Larder, Brendan (2011). "Clinical Evaluation of the Potential Utility of Computational Modeling as an HIV Treatment Selection Tool by Physicians with Considerable HIV Experience". AIDS Patient Care and STDs 25 (1): 29–36. doi:10.1089/apc.2010.0254.  http://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=3030912
  4. Revell, Andrew (2011). "The development of an expert system to predict virological response to HIV therapy as part of an online treatment support tool". AIDS 25 (15): 1855–1863. doi:10.1097/QAD.0b013e328349a9c2.  https://dx.doi.org/10.1097%2FQAD.0b013e328349a9c2
  5. Revell, Andrew (2010). "Modelling response to HIV therapy without a genotype: an argument for viral load monitoring in resource-limited settings". Journal of Antimicrobial Chemotherapy 65 (4): 605–607. doi:10.1093/jac/dkq032.  http://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=2837552
  6. Revell, Andrew et al. (2013). "Computational models can predict response to HIV therapy without a genotype and may reduce treatment failure in different resource-limited settings". Journal of Antimicrobial Chemotherapy. doi:10.1093/jac/dkt041.  http://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=3654223
  7. Larder, Brendan et al. (2013). "Accurate prediction of response to HIV therapy without a genotype a potential tool for therapy optimisation in resource-limited settings". Antiviral Therapy. 
  8. Revell, Andrew et al. (2013). "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". BioMed Research International 2013. doi:10.1155/2013/579741.  https://dx.doi.org/10.1155%2F2013%2F579741
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