ArticleNature communications2025
Machine learning in Alzheimer's disease genetics.
Article in Nature communications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Golgi dysfunction in Alzheimer disease: from human multiomic signatures to therapeutic targets.Experimental & molecular medicine · 2026Review
- Sex-dependent interferon signaling contributes to female-biased vulnerability in Alzheimer's disease.Journal of neuroinflammation · 2026Article
- Comparison and validation of multiple machine learning algorithms for predicting MDRO infection in catheter-related bloodstream patients: a multicenter cohort study.Microbiology spectrum · 2026Article
- Immunometabolism Reframes Alzheimer's Disease: From Systemic Dysmetabolism to Glial Rewiring.Cellular and molecular neurobiology · 2026Review
- Machine learning for Alzheimer's disease progression under extreme class imbalance.Frontiers in neuroscience · 2026Article
- Alzheimer's disease: genetic background in the era of next-generation sequencing technologies.Brain communications · 2026Review
- Identification of hidden subtypes in occupational health examinations and their biomedical characteristics using graph-enhanced deep representation learning.Frontiers in public health · 2026Article
- Leveraging a machine learning model to predict hospital readmission risk: integrating clinical and social determinants of health data.Frontiers in public health · 2026Article
- Unsupervised machine learning integrates genomic variants and EMR to unravel mechanisms of brain hemorrhage and epilepsy as early indicators of Alzheimer's in down syndrome.Alzheimer's research & therapy · 2025Article
Corrections and comments
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Authors and funding
113 authors.
Funding
Abstract
Traditional statistical approaches have advanced our understanding of the genetics of complex diseases, yet are limited to linear additive models. Here we applied machine learning (ML) to genome-wide data from 41,686 individuals in the largest European consortium on Alzheimer's disease (AD) to investigate the effectiveness of various ML algorithms in replicating known findings, discovering novel loci, and predicting individuals at risk. We utilised Gradient Boosting Machines (GBMs), biological pathway-informed Neural Networks (NNs), and Model-based Multifactor Dimensionality Reduction (MB-MDR) models. ML approaches successfully captured all genome-wide significant genetic variants identified in the training set and 22% of associations from larger meta-analyses. They highlight 6 novel loci which replicate in an external dataset, including variants which map to ARHGAP25, LY6H, COG7, SOD1 and ZNF597. They further identify novel association in AP4E1, refining the genetic landscape of the known SPPL2A locus. Our results demonstrate that machine learning methods can achieve predictive performance comparable to classical approaches in genetic epidemiology and have the potential to uncover novel loci that remain undetected by traditional GWAS. These insights provide a complementary avenue for advancing the understanding of AD genetics.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.