ArticlePLoS computational biology2020
A practical application of generative adversarial networks for RNA-seq analysis to predict the molecular progress of Alzheimer's disease.
Article in PLoS computational biology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 1 of them a synthesis that pooled it.
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Who cites it
20 citing papers in PubMed, 1 synthesis or guideline pooled it, 54 citations in OpenAlex.
- Diagnostic Performance of Generative Adversarial Network-Based Deep Learning Methods for Alzheimer's Disease: A Systematic Review and Meta-Analysis.Frontiers in aging neuroscience · 2022Pooled it
- Data-intensive immune network modelling for One Health.Briefings in bioinformatics · 2026Review
- Utilisation of Machine Learning Approaches Improves RNA-Seq Transcriptome Analyses in Alzheimer's Disease Brain.Journal of molecular neuroscience : MN · 2026Article
- A review of AI-based radiogenomics in neurodegenerative disease.Frontiers in big data · 2025Review
- Deep learning-based design and experimental validation of a medicine-like human antibody library.Briefings in bioinformatics · 2024Article
- A novel generative adversarial networks modelling for the class imbalance problem in high dimensional omics data.BMC medical informatics and decision making · 2024Article
- GAN Learning Methods for Bulk RNA-Seq Data and Their Interpretive Application in the Context of Disease Progression.Methods in molecular biology (Clifton, N.J.) · 2024Article
- GAN-WGCNA: Calculating gene modules to identify key intermediate regulators in cocaine addiction.PloS one · 2024Article
- GAN-based data augmentation for transcriptomics: survey and comparative assessment.Bioinformatics (Oxford, England) · 2023Article
- DHCR24 reverses Alzheimer's disease-related pathology and cognitive impairment via increasing hippocampal cholesterol levels in 5xFAD mice.Acta neuropathologica communications · 2023Article
- MTM: a multi-task learning framework to predict individualized tissue gene expression profiles.Bioinformatics (Oxford, England) · 2023Article
- Investigation of early molecular alterations in tauopathy with generative adversarial networks.Scientific reports · 2023Article
- Unveiling OASIS family as a key player in hypoxia-ischemia cases induced by cocaine using generative adversarial networks.Scientific reports · 2022Article
- The role of DHCR24 in the pathogenesis of AD: re-cognition of the relationship between cholesterol and AD pathogenesis.Acta neuropathologica communications · 2022Review
- Multi-omics data integration by generative adversarial network.Bioinformatics (Oxford, England) · 2021Article
- Advancing diagnostic performance and clinical applicability of deep learning-driven generative adversarial networks for Alzheimer's disease.Psychoradiology · 2021Article
- Deep Learning with Neuroimaging and Genomics in Alzheimer's Disease.International journal of molecular sciences · 2021Review
- Is Seeing Still Believing? Leveraging Deepfake Technology for Livestock Farming.Frontiers in veterinary science · 2021Review
- A primer on machine learning techniques for genomic applications.Computational and structural biotechnology journal · 2021Review
- Preferred Endocytosis of Amyloid Precursor Protein from Cholesterol-Enriched Lipid Raft Microdomains.Molecules (Basel, Switzerland) · 2020Article
Corrections and comments
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Authors and funding
4 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Next-generation sequencing (NGS) technology has become a powerful tool for dissecting the molecular and pathological signatures of a variety of human diseases. However, the limited availability of biological samples from different disease stages is a major hurdle in studying disease progressions and identifying early pathological changes. Deep learning techniques have recently begun to be applied to analyze NGS data and thereby predict the progression of biological processes. In this study, we applied a deep learning technique called generative adversarial networks (GANs) to predict the molecular progress of Alzheimer's disease (AD). We successfully applied GANs to analyze RNA-seq data from a 5xFAD mouse model of AD, which recapitulates major AD features of massive amyloid-β (Aβ) accumulation in the brain. We examined how the generator is featured to have specific-sample generation and biological gene association. Based on the above observations, we suggested virtual disease progress by latent space interpolation to yield the transition curves of various genes with pathological changes from normal to AD state. By performing pathway analysis based on the transition curve patterns, we identified several pathological processes with progressive changes, such as inflammatory systems and synapse functions, which have previously been demonstrated to be involved in the pathogenesis of AD. Interestingly, our analysis indicates that alteration of cholesterol biosynthesis begins at a very early stage of AD, suggesting that it is the first effect to mediate the cholesterol metabolism of AD downstream of Aβ accumulation. Here, we suggest that GANs are a useful tool to study disease progression, leading to the identification of early pathological signatures.
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