ArticleBiomolecules2023
A New Approach for Multimodal Usage of Gene Expression and Its Image Representation for the Detection of Alzheimer's Disease.
Article in Biomolecules, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
5 citing papers in PubMed.
- Diagnosis of Alzheimer's disease with high accuracy via Petri net modeling of signaling pathways.Scientific reports · 2026Article
- A review of AI-based radiogenomics in neurodegenerative disease.Frontiers in big data · 2025Review
- Bioinformatics and experimental validation identify biomarkers for diagnosing Alzheimer's disease.Frontiers in aging neuroscience · 2025Article
- Enhanced analysis of tabular data through Multi-representation DeepInsight.Scientific reports · 2024Article
- Review
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Alzheimer's disease (AD) is a complex neurodegenerative disorder and the multifaceted nature of it requires innovative approaches that integrate various data modalities to enhance its detection. However, due to the cost of collecting multimodal data, multimodal datasets suffer from an insufficient number of samples. To mitigate the impact of a limited sample size on classification, we introduce a novel deep learning method (
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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 Socratic graph.