ArticleCommunications medicine2023
Deep learning-based polygenic risk analysis for Alzheimer's disease prediction.
Article in Communications medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 43 papers, 1 of them a synthesis that pooled it.
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Who cites it
43 citing papers in PubMed, 1 synthesis or guideline pooled it, 64 citations in OpenAlex.
- The application of artificial intelligence in diagnosis of Alzheimer's disease: a bibliometric analysis.Frontiers in neurology · 2024Pooled it
- Machine learning methodology using a masked neural network for robust genetic risk score calculation from noisy and missing data.Briefings in bioinformatics · 2026Article
- Exploring the use of machine and deep learning in genome-wide association studies: a comprehensive review.BioData mining · 2026Review
- Data Representation Bias and Conditional Distribution Shift Drive Predictive Performance Disparities in Multi-Population Machine Learning.bioRxiv : the preprint server for biology · 2026Article
- Clinical translation of polygenic scores for prostate cancer screening.Nature reviews. Urology · 2026Review
- Genetic analysis of neurodegenerative diseases.The Journal of clinical investigation · 2026Review
- A machine learning framework predicts oncogenic driver mutations from SNP profiles in lung adenocarcinoma.Discover oncology · 2026Article
- Education Research: Bridging the Artificial Intelligence Training Gap: Evidence from a National Survey of Italian Neurology Residents.Neurology. Education · 2026Article
- Interpreting artificial neural networks to detect genome-wide association signals for complex traits.NAR genomics and bioinformatics · 2026Article
- A deep ensemble encoder network method for improved polygenic risk score prediction.BioData mining · 2026Article
- Polygenic risk scores: Navigating the future of precision medicine through economic, ethical, and scientific advancements.iScience · 2026Review
- Clinical Application of Pharmacogenomics in Stroke Management: Current Evidence and Future Directions.Journal of stroke · 2026Review
- Alzheimer's disease: genetic background in the era of next-generation sequencing technologies.Brain communications · 2026Review
- Benchmarking interpretability of deep learning for predictive genomics: Recall, precision, and variability of feature attribution.PLoS computational biology · 2025Article
- Leveraging functional annotations to map rare variants associated with Alzheimer disease with gruyere.American journal of human genetics · 2025Article
- Vulnerability of mitochondrial OXPHOS complexes in the arcuate nucleus of the hypothalamus of Alzheimer's disease.Journal of Alzheimer's disease : JAD · 2025Article
- A survey on deep learning for polygenic risk scores.Briefings in bioinformatics · 2025Review
- Performance of deep-learning-based approaches to improve polygenic scores.Nature communications · 2025Article
- Development of a Japanese polygenic risk score model for amyloid-β PET imaging in Alzheimer's disease.Alzheimer's research & therapy · 2025Article
- VADEr: Vision Transformer-Inspired Framework for Polygenic Risk Reveals Underlying Genetic Heterogeneity in Prostate Cancer.medRxiv : the preprint server for health sciences · 2025Article
Corrections and comments
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Authors and funding
24 authors at 20 institutions in 6 countries.
Funding
Abstract
backgroundThe polygenic nature of Alzheimer's disease (AD) suggests that multiple variants jointly contribute to disease susceptibility. As an individual's genetic variants are constant throughout life, evaluating the combined effects of multiple disease-associated genetic risks enables reliable AD risk prediction. Because of the complexity of genomic data, current statistical analyses cannot comprehensively capture the polygenic risk of AD, resulting in unsatisfactory disease risk prediction. However, deep learning methods, which capture nonlinearity within high-dimensional genomic data, may enable more accurate disease risk prediction and improve our understanding of AD etiology. Accordingly, we developed deep learning neural network models for modeling AD polygenic risk.
methodsWe constructed neural network models to model AD polygenic risk and compared them with the widely used weighted polygenic risk score and lasso models. We conducted robust linear regression analysis to investigate the relationship between the AD polygenic risk derived from deep learning methods and AD endophenotypes (i.e., plasma biomarkers and individual cognitive performance). We stratified individuals by applying unsupervised clustering to the outputs from the hidden layers of the neural network model.
resultsThe deep learning models outperform other statistical models for modeling AD risk. Moreover, the polygenic risk derived from the deep learning models enables the identification of disease-associated biological pathways and the stratification of individuals according to distinct pathological mechanisms.
conclusionOur results suggest that deep learning methods are effective for modeling the genetic risks of AD and other diseases, classifying disease risks, and uncovering disease mechanisms.
Identifiers
What OpenQuestion holds
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.