The use of class imbalanced learning methods on ULSAM data to predict the case–control status in genome-wide association studies

The use of class imbalanced learning methods on ULSAM data to predict the case–control status in genome-wide association studies

Abstract Machine learning (ML) methods for uncovering single nucleotide polymorphisms (SNPs) in genome-wide association study (GWAS) data that can be used to predict disease outcomes are becoming increasingly used in genetic research. Two issues with the use of ML models are finding the correct meth...

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Bibliographic Details
Main Authors: R. Onur Öztornaci, Hamzah Syed, Andrew P. Morris, Bahar Taşdelen
Format: Article
Language:English
Published: SpringerOpen 2023-11-01
Series:Journal of Big Data
Subjects:
Machine learning
Class imbalanced methods
GWAS
ULSAM study
Online Access:https://doi.org/10.1186/s40537-023-00853-x

Internet

https://doi.org/10.1186/s40537-023-00853-x

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