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Can I read A Machine Learning-Based Identification of Genes Affecting the Pharmacokinetics of Tacrolimus Using the DMET^TM^ Plus Platform on EtoBox?
A Machine Learning-Based Identification of Genes Affecting the Pharmacokinetics of Tacrolimus Using the DMET^TM^ Plus Platform by Jeong-An Gim; Yonghan Kwon; Hyun A Lee; Kyeong-Ryoon Lee; Soohyun Kim; Yoonjung Choi; Yu Kyong Kim; Howard Lee is a Biochemistry, Genetics and Molecular Biology article available to read on EtoBox.
What is A Machine Learning-Based Identification of Genes Affecting the Pharmacokinetics of Tacrolimus Using the DMET^TM^ Plus Platform about?
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.
Who reads A Machine Learning-Based Identification of Genes Affecting the Pharmacokinetics of Tacrolimus Using the DMET^TM^ Plus Platform?
It is typically read by researchers, students, and practitioners in Biochemistry, Genetics and Molecular Biology.
- Author
- Jeong-An Gim; Yonghan Kwon; Hyun A Lee; Kyeong-Ryoon Lee; Soohyun Kim; Yoonjung Choi; Yu Kyong Kim; Howard Lee
- Publisher
- Molecular Diversity Preservation International; MDPI AG; Multidisciplinary Digital Publishing Institute (MDPI); Basel: MDPI Center, 2000- (ISSN 1422-0067)
- Published
- 2020
- Language
- EN
- Field
- Biochemistry, Genetics and Molecular Biology (Life Sciences)