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Article

Machine learning-based quantification for disease uncertainty increases the statistical power of genetic association studies.

Bioinformatics (Oxford, England) - 2 Sept 2023

Park Jun Young, Lee Jang Jae, Lee Younghwa, Lee Dongsoo, Gim Jungsoo, Farrer Lindsay, Lee Kun Ho, Won Sungho

Abstract excerpt

MOTIVATION: Allowance for increasingly large samples is a key to identify the association of genetic variants with Alzheimer's disease (AD) in genome-wide association studies (GWAS). Accordingly, we aimed to develop a method that incorporates patients with mild cognitive impairment and unknown cognitive status in GWAS using a machine learning-based AD prediction model. RESULTS: Simulation analyses showed that...

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Identifiers

PMID: 37665736
DOI: 10.1093/bioinformatics/btad534
PMCID: PMC10539075

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 7 Sept 2026. Manifest publication-graph-cohort-2026-09-07.1.

Source receipt: pubmed, retrieved 7 Sept 2026.

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