Computational models can predict response to HIV therapy without a genotype and may reduce treatment failure in different resource-limited settings.

@article{Revell2013ComputationalMC,
  title={Computational models can predict response to HIV therapy without a genotype and may reduce treatment failure in different resource-limited settings.},
  author={Andrew Revell and Dechao Wang and Robin Wood and Carl Morrow and Hugo A. Tempelman and Raph L. Hamers and Gerardo Alvarez-Uria and Adrian Streinu-Cercel and Luminița Ene and Annemarie M. J. Wensing and F W Dewolf and Mark Nelson and Julio S. G. Montaner and H. Clifford Lane and Brendan Larder},
  journal={The Journal of antimicrobial chemotherapy},
  year={2013},
  volume={68 6},
  pages={1406-14}
}
OBJECTIVES Genotypic HIV drug-resistance testing is typically 60%-65% predictive of response to combination antiretroviral therapy (ART) and is valuable for guiding treatment changes. Genotyping is unavailable in many resource-limited settings (RLSs). We aimed to develop models that can predict response to ART without a genotype and evaluated their potential as a treatment support tool in RLSs. METHODS Random forest models were trained to predict the probability of response to ART (≤400… CONTINUE READING
Recent Discussions
This paper has been referenced on Twitter 7 times over the past 90 days. VIEW TWEETS