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RSS FeedsIJMS, Vol. 21, Pages 2517: A Machine Learning-Based Identification of Genes Affecting the Pharmacokinetics of Tacrolimus using the DMETTM Plus Platform (International Journal of Molecular Sciences)

 
 

4 april 2020 23:02:26

 
IJMS, Vol. 21, Pages 2517: A Machine Learning-Based Identification of Genes Affecting the Pharmacokinetics of Tacrolimus using the DMETTM Plus Platform (International Journal of Molecular Sciences)
 


Tacrolimus is an immunosuppressive drug with a narrow therapeutic index and larger interindividual variability. We identified genetic variants to predict tacrolimus exposure in healthy Korean males using machine learning algorithms such as decision tree, random forest, and least absolute shrinkage and selection operator (LASSO) regression. rs776746 (CYP3A5) and rs1137115 (CYP2A6) are single nucleotide polymorphisms (SNPs) that can affect exposure to tacrolimus. A decision tree, when coupled with random forest analysis, is an efficient tool for predicting the exposure to tacrolimus based on genotype. These tools are helpful to determine an individualized dose of tacrolimus.


 
49 viewsCategory: Biochemistry, Biophysics, Molecular Biology
 
IJMS, Vol. 21, Pages 2518: Dysbacteriosis-Derived Lipopolysaccharide Causes Embryonic Osteopenia through Retinoic-Acid-Regulated DLX5 Expression (International Journal of Molecular Sciences)
IJMS, Vol. 21, Pages 2534: In Silico Studies on Triterpenoid Saponins Permeation through the Blood-Brain Barrier Combined with Postmortem Research on the Brain Tissues of Mice Affected by Astragaloside IV Administration (International Journal of Molecular Sciences)
 
 
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