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Abstract:
\sectionMotivation:}
Due to the high mutation rate of HIV, drug resistant variants emerge
frequently. Therefore, researchers are constantly searching for new ways to
attack the virus. One new class of anti-HIV drugs is the class of coreceptor
antagonists that block cell entry by occupying a coreceptor on CD4 cells. This
type of drug just has an effect on the subset of HIVs that use the inhibited
coreceptor. A good prediction of whether the viral population inside a patient
is susceptible to the treatment is hence very important for therapy decisions
and prerequisite to administering the respective drug. The first prediction
models were based on data from Sanger sequencing of the V3 loop of HIV.
Recently, a method based on next generation sequencing (NGS) data was
introduced that predicts labels for each read separately and decides on the
patient label via a percentage threshold for the resistant viral minority.
\section{Results:}
We model the prediction problem on the patient level taking the information of
all reads from NGS data jointly into account. This enables us to improve
prediction performance for NGS data, but we can also use the trained model to
improve predictions based on Sanger sequencing data. Therefore, also
laboratories without next generation sequencing capabilities can benefit from
the improvements. Furthermore, we show which amino acids at which position are
important for prediction success, giving clues on how the interaction mechanism
between the V3 loop and the particular coreceptors might be influenced.
\section{Availability:}
A webserver is available at http://coreceptor.bioinf.mpi-inf.mpg.de.
\href{http://coreceptor.bioinf.mpi-inf.mpg.de/}{
http://coreceptor.bioinf.mpi-inf.mpg.de/}.
\section{Contact:}
\href{nico.pfeifer@mpi-inf.mpg.de}{nico.pfeifer@mpi-inf.mpg.de
