ArticleBMC bioinformatics2019
Benchmarking machine learning models for late-onset alzheimer's disease prediction from genomic data.
Article in BMC bioinformatics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 papers.
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Who cites it
36 citing papers in PubMed.
- Artificial Intelligence for Alzheimer's Disease Diagnosis: From Traditional Machine Learning to Large Language Models.Biosensors · 2026Review
- Machine learning-driven discovery of host genetic factors for paratuberculosis in goats within the one health framework.BMC veterinary research · 2026Article
- DuAL-Net: A Dual-Network Approach for Alzheimer's Disease Risk Prediction UsingComputational and structural biotechnology journal · 2026Article
- Cross-cohort genetic risk prediction for Alzheimer's disease: a transfer learning approach using GWAS and deep learning models.BioData mining · 2025Article
- Beyond Transgenic Mice: Emerging Models and Translational Strategies in Alzheimer's Disease.International journal of molecular sciences · 2025Review
- Article
- Machine Learning Methods for Classifying Multiple Sclerosis and Alzheimer's Disease Using Genomic Data.International journal of molecular sciences · 2025Article
- Prediction of mild cognitive impairment using blood multi-omics data.Frontiers in genetics · 2025Article
- Predicting early Alzheimer's with blood biomarkers and clinical features.Scientific reports · 2024Article
- A Comprehensive Review on Synergy of Multi-Modal Data and AI Technologies in Medical Diagnosis.Bioengineering (Basel, Switzerland) · 2024Review
- A simulative deep learning model of SNP interactions on chromosome 19 for predicting Alzheimer's disease risk and rates of disease progression.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2023Article
- Artificial intelligence for dementia genetics and omics.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2023Review
- c-Diadem: a constrained dual-input deep learning model to identify novel biomarkers in Alzheimer's disease.BMC medical genomics · 2023Article
- Classification and deep-learning-based prediction of Alzheimer disease subtypes by using genomic data.Translational psychiatry · 2023Article
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- Article
- X chromosome-wide association study of quantitative biomarkers from the Alzheimer's Disease Neuroimaging Initiative study.Frontiers in aging neuroscience · 2023Article
- Wide and deep learning based approaches for classification of Alzheimer's disease using genome-wide association studies.PloS one · 2023Article
- A Computerized Analysis with Machine Learning Techniques for the Diagnosis of Parkinson's Disease: Past Studies and Future Perspectives.Diagnostics (Basel, Switzerland) · 2022Review
- On the limits of graph neural networks for the early diagnosis of Alzheimer's disease.Scientific reports · 2022Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
backgroundLate-Onset Alzheimer's Disease (LOAD) is a leading form of dementia. There is no effective cure for LOAD, leaving the treatment efforts to depend on preventive cognitive therapies, which stand to benefit from the timely estimation of the risk of developing the disease. Fortunately, a growing number of Machine Learning methods that are well positioned to address this challenge are becoming available.
resultsWe conducted systematic comparisons of representative Machine Learning models for predicting LOAD from genetic variation data provided by the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort. Our experimental results demonstrate that the classification performance of the best models tested yielded ∼72% of area under the ROC curve.
conclusionsMachine learning models are promising alternatives for estimating the genetic risk of LOAD. Systematic machine learning model selection also provides the opportunity to identify new genetic markers potentially associated with the disease.
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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 OpenQuestion graph.