Evaluation of penalized and machine learning methods for asthma disease prediction in the Korean Genome and Epidemiology Study (KoGES)

Evaluation of penalized and machine learning methods for asthma disease prediction in the Korean Genome and Epidemiology Study (KoGES)

Abstract Background Genome-wide association studies have successfully identified genetic variants associated with human disease. Various statistical approaches based on penalized and machine learning methods have recently been proposed for disease prediction. In this study, we evaluated the performa...

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Bibliographic Details
Main Authors: Yongjun Choi, Junho Cha, Sungkyoung Choi
Format: Article
Language:English
Published: BMC 2024-02-01
Series:BMC Bioinformatics
Subjects:
Disease risk prediction model
Large-scale genetic data
Asthma
Penalized methods
Machine learning methods
Ensemble methods
Online Access:https://doi.org/10.1186/s12859-024-05677-x

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