ArticleFrontiers in genetics2021
Improving the Classification of Alzheimer's Disease Using Hybrid Gene Selection Pipeline and Deep Learning.
Article in Frontiers in genetics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 1 of them a synthesis that pooled it.
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Who cites it
15 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Neural Computation-Based Methods for the Early Diagnosis and Prognosis of Alzheimer's Disease Not Using Neuroimaging Biomarkers: A Systematic Review.Journal of Alzheimer's disease : JAD · 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
- Discovering non-linear dynamics of miRNAs in Alzheimer's disease-related cognitive impairment: a cross-species approach with explainable machine learning.Biology direct · 2026Article
- LASSO-HHO two-stage hybrid gene selection framework for accurate Alzheimer's disease diagnosis.Scientific reports · 2026Article
- Alzheimer's disease prediction using deep learning and XAI based interpretable feature selection from blood gene expression data.Scientific reports · 2026Article
- Are the tools fit for purpose? Network inference algorithms evaluated on a simulated lipidomics network.Bioinformatics advances · 2025Article
- A review of AI-based radiogenomics in neurodegenerative disease.Frontiers in big data · 2025Review
- Improving machine learning detection of Alzheimer disease using enhanced manta ray gene selection of Alzheimer gene expression datasets.PeerJ. Computer science · 2025Article
- Improving Alzheimer's Disease Prediction with Different Machine Learning Approaches and Feature Selection Techniques.Diagnostics (Basel, Switzerland) · 2024Article
- Exploring Huntington's Disease Diagnosis via Artificial Intelligence Models: A Comprehensive Review.Diagnostics (Basel, Switzerland) · 2023Review
- Leveraging Computational Intelligence Techniques for Diagnosing Degenerative Nerve Diseases: A Comprehensive Review, Open Challenges, and Future Research Directions.Diagnostics (Basel, Switzerland) · 2023Review
- A Machine Learning Approach for Early Diagnosis of Cognitive Impairment Using Population-Based Data.Journal of Alzheimer's disease : JAD · 2023Article
- Prediction of Alzheimer's Disease by a Novel Image-Based Representation of Gene Expression.Genes · 2022Article
- A Transfer Learning Method for Detecting Alzheimer's Disease Based on Speech and Natural Language Processing.Frontiers in public health · 2022Article
- Identification of Dementia & Mild Cognitive Impairment in Chinese Elderly Using Machine Learning.American journal of Alzheimer's disease and other dementiasArticle
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4 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Alzheimer's is a progressive, irreversible, neurodegenerative brain disease. Even with prominent symptoms, it takes years to notice, decode, and reveal Alzheimer's. However, advancements in technologies, such as imaging techniques, help in early diagnosis. Still, sometimes the results are inaccurate, which delays the treatment. Thus, the research in recent times focused on identifying the molecular biomarkers that differentiate the genotype and phenotype characteristics. However, the gene expression dataset's generated features are huge, 1,000 or even more than 10,000. To overcome such a curse of dimensionality, feature selection techniques are introduced. We designed a gene selection pipeline combining a filter, wrapper, and unsupervised method to select the relevant genes. We combined the minimum Redundancy and maximum Relevance (mRmR), Wrapper-based Particle Swarm Optimization (WPSO), and Auto encoder to select the relevant features. We used the GSE5281 Alzheimer's dataset from the Gene Expression Omnibus We implemented an Improved Deep Belief Network (IDBN) with simple stopping criteria after choosing the relevant genes. We used a Bayesian Optimization technique to tune the hyperparameters in the Improved Deep Belief Network. The tabulated results show that the proposed pipeline shows promising results.
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