ArticlePloS one2023
Wide and deep learning based approaches for classification of Alzheimer's disease using genome-wide association studies.
Article in PloS one, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed, 14 citations in OpenAlex.
- Multimodal deep learning enhances genomic risk prediction for cardiometabolic diseases.Briefings in bioinformatics · 2026Article
- Exploring the use of machine and deep learning in genome-wide association studies: a comprehensive review.BioData mining · 2026Review
- DAG-VAERL: a novel causal inference method for building causal gene regulatory networks.BioData mining · 2026Article
- Genetic risk in Alzheimer's disease.NPJ systems biology and applications · 2026Review
- Genome-wide association study bridging genomics-phenomics gap in natural plant populations.Journal of applied genetics · 2026Review
- Cross-cohort genetic risk prediction for Alzheimer's disease: a transfer learning approach using GWAS and deep learning models.BioData mining · 2025Article
- Enhancing genomic disorder prediction through Feynman Concordance and Interpolated Nearest Centroid techniques.Scientific reports · 2024Article
- Cancer research in the United Arab Emirates from birth to present: A bibliometric analysis.Heliyon · 2024Article
- Predicting early Alzheimer's with blood biomarkers and clinical features.Scientific reports · 2024Article
- Review
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Authors and funding
5 authors at 3 institutions in 3 countries.
Funding
Abstract
The increasing incidence of Alzheimer's disease (AD) has been leading towards a significant growth in socioeconomic challenges. A reliable prediction of AD might be useful to mitigate or at-least slow down its progression for which, identification of the factors affecting the AD and its accurate diagnoses, are vital. In this study, we use Genome-Wide Association Studies (GWAS) dataset which comprises significant genetic markers of complex diseases. The original dataset contains large number of attributes (620901) for which we propose a hybrid feature selection approach based on association test, principal component analysis, and the Boruta algorithm, to identify the most promising predictors of AD. The selected features are then forwarded to a wide and deep neural network models to classify the AD cases and healthy controls. The experimental outcomes indicate that our approach outperformed the existing methods when evaluated on standard dataset, producing an accuracy and f1-score of 99%. The outcomes from this study are impactful particularly, the identified features comprising AD-associated genes and a reliable classification model that might be useful for other chronic diseases.
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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.