ArticleAlzheimer's & dementia (Amsterdam, Netherlands)2022
Machine learning methods applied to genotyping data capture interactions between single nucleotide variants in late onset Alzheimer's disease.
Article in Alzheimer's & dementia (Amsterdam, Netherlands), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed, 9 citations in OpenAlex.
- Genetic risk in Alzheimer's disease.NPJ systems biology and applications · 2026Review
- Cross-cohort genetic risk prediction for Alzheimer's disease: a transfer learning approach using GWAS and deep learning models.BioData mining · 2025Article
- c-Diadem: a constrained dual-input deep learning model to identify novel biomarkers in Alzheimer's disease.BMC medical genomics · 2023Article
- Wide and deep learning based approaches for classification of Alzheimer's disease using genome-wide association studies.PloS one · 2023Article
- Machine learning methods applied to genotyping data capture interactions between single nucleotide variants in late onset Alzheimer's disease.Alzheimer's & dementia (Amsterdam, Netherlands) · 2022Article
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Authors and funding
11 authors at 5 institutions in 5 countries.
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Abstract
Introduction: Genome-wide association studies (GWAS) in late onset Alzheimer's disease (LOAD) provide lists of individual genetic determinants. However, GWAS do not capture the synergistic effects among multiple genetic variants and lack good specificity. Methods: We applied tree-based machine learning algorithms (MLs) to discriminate LOAD (>700 individuals) and age-matched unaffected subjects in UK Biobank with single nucleotide variants (SNVs) from Alzheimer's disease (AD) studies, obtaining specific genomic profiles with the prioritized SNVs. Results: MLs prioritized a set of SNVs located in genes Discussion: Our approach efficiently discriminates LOAD from controls, capturing genomic profiles defined by interactions among SNVs in a hot-spot region.
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