ArticleTranslational psychiatry2023
Classification and deep-learning-based prediction of Alzheimer disease subtypes by using genomic data.
Article in Translational psychiatry, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed.
- Exploring the use of machine and deep learning in genome-wide association studies: a comprehensive review.BioData mining · 2026Review
- Genetic Investigation of Corrected QT Interval Sensitivity to Oral Bepridil Hydrochloride Hydrate in Patients With Atrial Fibrillation.Journal of the American Heart Association · 2026Article
- Genetic Variants Associated With Nonpulmonary Vein Triggers of Atrial Fibrillation: A Genome-Wide Association Study.JACC. Advances · 2026Article
- Genetic risk in Alzheimer's disease.NPJ systems biology and applications · 2026Review
- Uncertainty-aware genomic classification of Alzheimer's disease: a transformer-based ensemble approach with Monte Carlo dropout.Briefings in bioinformatics · 2025Article
- Genome-Wide Association Study of Atrial Fibrillation Recurrence After Radiofrequency Catheter Ablation in a Japanese Population.Journal of cardiovascular electrophysiology · 2025Article
- Machine Learning Methods for Classifying Multiple Sclerosis and Alzheimer's Disease Using Genomic Data.International journal of molecular sciences · 2025Article
- Advancements in genetic research by the Hispanic Community Health Study/Study of Latinos: A 10-year retrospective review.HGG advances · 2025Review
- Recent Advances in the Application of Artificial Intelligence in Alzheimer's Disease.Current Alzheimer research · 2025Review
- LD-informed deep learning for Alzheimer's gene loci detection using WGS data.medRxiv : the preprint server for health sciences · 2024Article
- Network dynamics-based subtyping of Alzheimer's disease with microglial genetic risk factors.Alzheimer's research & therapy · 2024Article
- The HLA-DRB1*09:01-DQB1*03:03 haplotype is associated with the risk for late-onset Alzheimer's disease in APOEnpj aging · 2024Article
- Deep Learning-Based Prediction of Alzheimer's Disease Using Microarray Gene Expression Data.Biomedicines · 2023Article
- Machine learning identification and immune infiltration of disulfidptosis-related Alzheimer's disease molecular subtypes.Immunity, inflammation and disease · 2023Article
- LD-informed deep learning for Alzheimer's gene loci detection using WGS data.Alzheimer's & dementia (New York, N. Y.)Article
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Authors and funding
8 authors.
Funding
Abstract
Late-onset Alzheimer's disease (LOAD) is the most common multifactorial neurodegenerative disease among elderly people. LOAD is heterogeneous, and the symptoms vary among patients. Genome-wide association studies (GWAS) have identified genetic risk factors for LOAD but not for LOAD subtypes. Here, we examined the genetic architecture of LOAD based on Japanese GWAS data from 1947 patients and 2192 cognitively normal controls in a discovery cohort and 847 patients and 2298 controls in an independent validation cohort. Two distinct groups of LOAD patients were identified. One was characterized by major risk genes for developing LOAD (APOC1 and APOC1P1) and immune-related genes (RELB and CBLC). The other was characterized by genes associated with kidney disorders (AXDND1, FBP1, and MIR2278). Subsequent analysis of albumin and hemoglobin values from routine blood test results suggested that impaired kidney function could lead to LOAD pathogenesis. We developed a prediction model for LOAD subtypes using a deep neural network, which achieved an accuracy of 0.694 (2870/4137) in the discovery cohort and 0.687 (2162/3145) in the validation cohort. These findings provide new insights into the pathogenic mechanisms of LOAD.
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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 Socratic graph.