Author:

Keywords:

Anti-HIV Agents, Genotype, HIV Infections, HIV-1, Humans, Linear Models, Mutation, Pyridazines, Reverse Transcriptase Inhibitors, Science & Technology, Life Sciences & Biomedicine, Biochemical Research Methods, Biotechnology & Applied Microbiology, Mathematical & Computational Biology, Biochemistry & Molecular Biology, DRUG-SUSCEPTIBILITY PHENOTYPE, MODEL SELECTION, HYPERSUSCEPTIBILITY, GENOTYPE, Nitriles, Pyrimidines, 01 Mathematical Sciences, 06 Biological Sciences, 08 Information and Computing Sciences, Bioinformatics, 31 Biological sciences, 46 Information and computing sciences, 49 Mathematical sciences

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.