ArticleBiomedicines2023
Deep Learning-Based Prediction of Alzheimer's Disease Using Microarray Gene Expression Data.
Article in Biomedicines, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 1 of them a synthesis that pooled it.
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Who cites it
13 citing papers in PubMed, 1 synthesis or guideline pooled it, 38 citations in OpenAlex.
- Machine Learning Driven by Magnetic Resonance Imaging for the Classification of Alzheimer Disease Progression: Systematic Review and Meta-Analysis.JMIR aging · 2024Pooled it
- Novel approach to early prediction of alzheimer's disease progression using integrated deep regulatory genetic neural network and optimized deep belief networks.Scientific reports · 2026Article
- LASSO-HHO two-stage hybrid gene selection framework for accurate Alzheimer's disease diagnosis.Scientific reports · 2026Article
- Smart Kiosk for Nutritional Management of People With Diabetes in Underserved Communities: Development and Technical Evaluation.JMIR formative research · 2026Article
- Diagnosis of Alzheimer's disease with high accuracy via Petri net modeling of signaling pathways.Scientific reports · 2026Article
- Synergistic medical genetic evolutionary optimization and deep convolutional generative augmentation with SHAP-driven interpretability for precise Alzheimer's disease severity grading.Brain informatics · 2025Article
- A Multimodal Multi-Stage Deep Learning Model for the Diagnosis of Alzheimer's Disease Using EEG Measurements.Neurology international · 2025Article
- Advancing Alzheimer's Therapy: Computational strategies and treatment innovations.IBRO neuroscience reports · 2025Review
- Deficits of Alzheimer's Disease Neuropsychological Architecture Correlate with Specific Exosomal mRNA Expression: Evidence of a Continuum?International journal of molecular sciences · 2025Article
- Deep learning-based Alzheimer's disease detection using magnetic resonance imaging and gene expression data.PloS one · 2025Article
- Comparison of light gradient boosting and logistic regression for interactomic hub genes inFrontiers in oral health · 2025Article
- A review of AI-based radiogenomics in neurodegenerative disease.Frontiers in big data · 2025Review
- Methods in DNA methylation array dataset analysis: A review.Computational and structural biotechnology journal · 2024Review
Corrections and comments
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Authors and funding
3 authors at 3 institutions in 4 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Alzheimer's disease is a genetically complex disorder, and microarray technology provides valuable insights into it. However, the high dimensionality of microarray datasets and small sample sizes pose challenges. Gene selection techniques have emerged as a promising solution to this challenge, potentially revolutionizing AD diagnosis. The study aims to investigate deep learning techniques, specifically neural networks, in predicting Alzheimer's disease using microarray gene expression data. The goal is to develop a reliable predictive model for early detection and diagnosis, potentially improving patient care and intervention strategies. This study employed gene selection techniques, including Singular Value Decomposition (SVD) and Principal Component Analysis (PCA), to pinpoint pertinent genes within microarray datasets. Leveraging deep learning principles, we harnessed a Convolutional Neural Network (CNN) as our classifier for Alzheimer's disease (AD) prediction. Our approach involved the utilization of a seven-layer CNN with diverse configurations to process the dataset. Empirical outcomes on the AD dataset underscored the effectiveness of the PCA-CNN model, yielding an accuracy of 96.60% and a loss of 0.3503. Likewise, the SVD-CNN model showcased remarkable accuracy, attaining 97.08% and a loss of 0.2466. These results accentuate the potential of our method for gene dimension reduction and classification accuracy enhancement by selecting a subset of pertinent
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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.