The use of machine learning to improve the effectiveness of ANRS in predicting HIV drug resistance.
Loading...
Date
2016
Authors
Journal Title
Journal ISSN
Volume Title
Publisher
Abstract
BACKGROUD
HIV has placed a large burden of disease in developing countries. HIV drug resistance is
inevitable due to selective pressure. Computer algorithms have been proven to help in
determining optimal treatment for HIV drug resistance patients. One such algorithm is the
ANRS gold standard interpretation algorithm developed by the French National Agency for
AIDS Research AC11 Resistance group.
OBJECTIVES
The aim of this study is to investigate the possibility of improving the accuracy of the ANRS
gold standard in predicting HIV drug resistance.
METHODS
Data consisting of genome sequence and a HIV drug resistance measure was obtained from
the Stanford HIV database. Machine learning factor analysis was performed to determine
sequence positions where mutations lead to drug resistance. Sequence positions not found
in ANRS were added to the ANRS rules and accuracy was recalculated.
RESULTS
The machine learning algorithm did find sequence positions, not associated with ANRS, but
the model suggests they are important in the prediction of HIV drug resistance. Preliminary
results show that for IDV 10 sequence positions where found that were not associated
with ANRS rules, 4 for LPV, and 8 for NFV. For NFV, ANRS misclassified 74 resistant profiles
as being susceptible to the ARV. Sixty eight of the 74 sequences (92%) were classified as
resistance with the inclusion of the eight new sequence positions. No change was found
for LPV and a 78% improvement was associated with IDV.
CONCLUSION
The study shows that there is a possibility of improving ANRS accuracy.
Description
Master of TeleHealth in Medical Informatics. University of KwaZulu-Natal, Durban, 2016.