Comparing feature selection and machine learning approaches for predicting CYP2D6 methylation from genetic variation

Comparing feature selection and machine learning approaches for predicting CYP2D6 methylation from genetic variation

IntroductionPharmacogenetics 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 h...

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Bibliographic Details
Main Authors: Wei Jing Fong, Hong Ming Tan, Rishabh Garg, Ai Ling Teh, Hong Pan, Varsha Gupta, Bernadus Krishna, Zou Hui Chen, Natania Yovela Purwanto, Fabian Yap, Kok Hian Tan, Kok Yen Jerry Chan, Shiao-Yng Chan, Nicole Goh, Nikita Rane, Ethel Siew Ee Tan, Yuheng Jiang, Mei Han, Michael Meaney, Dennis Wang, Jussi Keppo, Geoffrey Chern-Yee Tan
Format: Article
Language:English
Published: Frontiers Media S.A. 2024-02-01
Series:Frontiers in Neuroinformatics
Subjects:
personalised medicine
genomics
epigenetics
CYP2D6
machine-learning
Online Access:https://www.frontiersin.org/articles/10.3389/fninf.2023.1244336/full

Internet

https://www.frontiersin.org/articles/10.3389/fninf.2023.1244336/full

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