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• W2171620714 abstract "Abstract Motivation: Due to the high mutation rate of human immunodeficiency virus (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 pre-requisite 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 through a percentage threshold for the resistant viral minority. 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 NGS 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. Availability: A webserver is available at http://coreceptor.bioinf.mpi-inf.mpg.de. Contact: nico.pfeifer@mpi-inf.mpg.de" @default.
• W2171620714 created "2016-06-24" @default.
• W2171620714 creator A5023556977 @default.
• W2171620714 creator A5076705834 @default.
• W2171620714 date "2012-09-03" @default.
• W2171620714 modified "2023-10-18" @default.
• W2171620714 title "Improving HIV coreceptor usage prediction in the clinic using hints from next-generation sequencing data" @default.
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• W2171620714 doi "https://doi.org/10.1093/bioinformatics/bts373" @default.
• W2171620714 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/3436800" @default.
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