Title: Cross-validated stepwise regression for identification of novel non-nucleoside reverse transcriptase inhibitor resistance associated mutations
Authors: Van der Borght, Koen ×
Van Craenenbroeck, Elke
Lecocq, Pierre
Van Houtte, Margriet
Van Kerckhove, Barbara
Bacheler, Lee
Verbeke, Geert
van Vlijmen, Herman #
Issue Date: 2011
Publisher: BioMed Central
Series Title: BMC Bioinformatics vol:12 pages:386
Article number: 10.1186/1471-2105-12-386
Abstract: Linear regression models are used to quantitatively predict drug resistance, the phenotype, from the HIV-1 viral genotype. As new antiretroviral drugs become available, new resistance pathways emerge and the number of resistance associated mutations continues to increase. To accurately identify which drug options are left, the main goal of the modeling has been to maximize predictivity and not interpretability. However, we originally selected linear regression as the preferred method for its transparency as opposed to other techniques such as neural networks. Here, we apply a method to lower the complexity of these phenotype prediction models using a 3-fold cross-validated selection of mutations.
ISSN: 1471-2105
Publication status: published
KU Leuven publication type: IT
Appears in Collections:Leuven Biostatistics and Statistical Bioinformatics Centre (L-BioStat)
× corresponding author
# (joint) last author

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