ArticleBriefings in bioinformatics2022
Deep learning-based identification of genetic variants: application to Alzheimer's disease classification.
Article in Briefings in bioinformatics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 37 papers, 1 of them a synthesis that pooled it.
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Who cites it
37 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Disease classification for whole-blood DNA methylation: Meta-analysis, missing values imputation, and XAI.GigaScience · 2022Pooled it
- Multimodal deep learning enhances genomic risk prediction for cardiometabolic diseases.Briefings in bioinformatics · 2026Article
- Genetic risk in Alzheimer's disease.NPJ systems biology and applications · 2026Review
- Detection and Classification of Alzheimer's Disease Using Deep and Machine Learning.Tomography (Ann Arbor, Mich.) · 2025Article
- Cross-cohort genetic risk prediction for Alzheimer's disease: a transfer learning approach using GWAS and deep learning models.BioData mining · 2025Article
- Uncertainty-aware genomic classification of Alzheimer's disease: a transformer-based ensemble approach with Monte Carlo dropout.Briefings in bioinformatics · 2025Article
- Review
- MetaGeno: a chromosome-wise multi-task genomic framework for ischaemic stroke risk prediction.Briefings in bioinformatics · 2025Article
- Improved Alzheimer Disease Diagnosis With a Machine Learning Approach and Neuroimaging: Case Study Development.JMIRx med · 2025Article
- Intelligent Alzheimer's diagnosis and disability assessment: robust medical imaging analysis using ensemble learning with ResNet-50 and EfficientNet-B3.Frontiers in medicine · 2025Article
- Overview of Alzheimer's Disease Neuroimaging Initiative and future clinical trials.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2025Review
- A review of AI-based radiogenomics in neurodegenerative disease.Frontiers in big data · 2025Review
- LD-informed deep learning for Alzheimer's gene loci detection using WGS data.medRxiv : the preprint server for health sciences · 2024Article
- Deciphering the genetic interplay between depression and dysmenorrhea: a Mendelian randomization study.Briefings in bioinformatics · 2024Article
- RiceSNP-ABST: a deep learning approach to identify abiotic stress-associated single nucleotide polymorphisms in rice.Briefings in bioinformatics · 2024Article
- RiceSNP-BST: a deep learning framework for predicting biotic stress-associated SNPs in rice.Briefings in bioinformatics · 2024Article
- Use of Artificial Intelligence in Improving Outcomes in Heart Disease: A Scientific Statement From the American Heart Association.Circulation · 2024Review
- A Comprehensive Review on Synergy of Multi-Modal Data and AI Technologies in Medical Diagnosis.Bioengineering (Basel, Switzerland) · 2024Review
- Evaluation of penalized and machine learning methods for asthma disease prediction in the Korean Genome and Epidemiology Study (KoGES).BMC bioinformatics · 2024Article
- An explainable machine learning approach for Alzheimer's disease classification.Scientific reports · 2024Article
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Authors and funding
5 authors.
Funding
Abstract
Deep learning is a promising tool that uses nonlinear transformations to extract features from high-dimensional data. Deep learning is challenging in genome-wide association studies (GWAS) with high-dimensional genomic data. Here we propose a novel three-step approach (SWAT-CNN) for identification of genetic variants using deep learning to identify phenotype-related single nucleotide polymorphisms (SNPs) that can be applied to develop accurate disease classification models. In the first step, we divided the whole genome into nonoverlapping fragments of an optimal size and then ran convolutional neural network (CNN) on each fragment to select phenotype-associated fragments. In the second step, using a Sliding Window Association Test (SWAT), we ran CNN on the selected fragments to calculate phenotype influence scores (PIS) and identify phenotype-associated SNPs based on PIS. In the third step, we ran CNN on all identified SNPs to develop a classification model. We tested our approach using GWAS data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) including (N = 981; cognitively normal older adults (CN) = 650 and AD = 331). Our approach identified the well-known APOE region as the most significant genetic locus for AD. Our classification model achieved an area under the curve (AUC) of 0.82, which was compatible with traditional machine learning approaches, random forest and XGBoost. SWAT-CNN, a novel deep learning-based genome-wide approach, identified AD-associated SNPs and a classification model for AD and may hold promise for a range of biomedical applications.
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