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Fast and scalable ensemble learning method for versatile polygenic risk prediction.

Proceedings of the National Academy of Sciences of the United States of America - 13 Aug 2024

Chen Tony, Zhang Haoyu, Mazumder Rahul, Lin Xihong

Abstract excerpt

Polygenic risk scores (PRS) enhance population risk stratification and advance personalized medicine, but existing methods face several limitations, encompassing issues related to computational burden, predictive accuracy, and adaptability to a wide range of genetic architectures. To address these issues, we propose Aggregated L0Learn using Summary-level data (ALL-Sum), a fast and scalable ensemble learning...

Topics

Humans
Multifactorial Inheritance
Genome-Wide Association Study
Machine Learning
Genetic Predisposition to Disease
Polymorphism, Single Nucleotide

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Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

Identifiers

PMID: 39110727
DOI: 10.1073/pnas.2403210121
PMCID: PMC11331062

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PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 31 Aug 2026.

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