ArticlePloS one2020
Improving predictive models for Alzheimer's disease using GWAS data by incorporating misclassified samples modeling.
Article in PloS one, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 2 of them syntheses that pooled it.
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Who cites it
15 citing papers in PubMed, 2 syntheses or guidelines pooled it, 33 citations in OpenAlex.
- Application of machine learning in measurement of ageing and geriatric diseases: a systematic review.BMC geriatrics · 2023Pooled it
- Exploring alternative approaches to precision medicine through genomics and artificial intelligence - a systematic review.Frontiers in medicine · 2023Pooled it
- A novel method to disentangle tightly linked risk and resilience genes for brain disorders: application to Alzheimer's disease.Molecular psychiatry · 2026Article
- Beyond Transgenic Mice: Emerging Models and Translational Strategies in Alzheimer's Disease.International journal of molecular sciences · 2025Review
- Machine Learning Methods for Classifying Multiple Sclerosis and Alzheimer's Disease Using Genomic Data.International journal of molecular sciences · 2025Article
- Explainable variational autoencoder (E-VAE) model using genome-wide SNPs to predict dementia.Journal of biomedical informatics · 2023Article
- Revealing the correlations between brain cortical characteristics and susceptibility genes for Alzheimer disease: a cross-sectional study.Quantitative imaging in medicine and surgery · 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
- Machine learning approaches for the genomic prediction of rheumatoid arthritis and systemic lupus erythematosus.BioData mining · 2021Article
- A Machine Learning Algorithm for Quantitatively Diagnosing Oxidative Stress Risks in Healthy Adult Individuals Based on Health Space Methodology: A Proof-of-Concept Study Using Korean Cross-Sectional Cohort Data.Antioxidants (Basel, Switzerland) · 2021Article
- Interplay between stress-related genes may influence Alzheimer's disease development: The results of genetic interaction analyses of human data.Mechanisms of ageing and development · 2021Article
- Machine learning for the life-time risk prediction of Alzheimer's disease: a systematic review.Brain communications · 2021Review
- Polygenic Score Models for Alzheimer's Disease: From Research to Clinical Applications.Frontiers in neuroscience · 2021Review
- Genetic variations analysis for complex brain disease diagnosis using machine learning techniques: opportunities and hurdles.PeerJ. Computer science · 2021Article
Corrections and comments
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Authors and funding
5 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Late-onset Alzheimer's Disease (LOAD) is the most common form of dementia in the elderly. Genome-wide association studies (GWAS) for LOAD have open new avenues to identify genetic causes and to provide diagnostic tools for early detection. Although several predictive models have been proposed using the few detected GWAS markers, there is still a need for improvement and identification of potential markers. Commonly, polygenic risk scores are being used for prediction. Nevertheless, other methods to generate predictive models have been suggested. In this research, we compared three machine learning methods that have been proved to construct powerful predictive models (genetic algorithms, LASSO, and step-wise) and propose the inclusion of markers from misclassified samples to improve overall prediction accuracy. Our results show that the addition of markers from an initial model plus the markers of the model fitted to misclassified samples improves the area under the receiving operative curve by around 5%, reaching ~0.84, which is highly competitive using only genetic information. The computational strategy used here can help to devise better methods to improve classification models for AD. Our results could have a positive impact on the early diagnosis of Alzheimer's disease.
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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.