Comparing feature selection and machine learning approaches for predicting methylation from genetic variation.
Journal:
Frontiers in neuroinformatics
Published Date:
Feb 21, 2024
Abstract
INTRODUCTION: Pharmacogenetics currently supports clinical decision-making on the basis of a limited number of variants in a few genes and may benefit paediatric prescribing where there is a need for more precise dosing. Integrating genomic information such as methylation into pharmacogenetic models holds the potential to improve their accuracy and consequently prescribing decisions. Cytochrome P450 2D6 () is a highly polymorphic gene conventionally associated with the metabolism of commonly used drugs and endogenous substrates. We thus sought to predict epigenetic loci from single nucleotide polymorphisms (SNPs) related to in children from the GUSTO cohort.
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