ArticleScientific reports2020
Prediction of Alzheimer's disease using blood gene expression data.
Article in Scientific reports, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 70 papers, 2 of them syntheses that pooled it.
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Who cites it
70 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Meta-Analysis of Transcriptomic Studies of Blood and Six Brain Regions Identifies a Consensus of 15 Cross-Tissue Mechanisms in Alzheimer's Disease and Suggests an Origin of Cross-Study Heterogeneity.American journal of medical genetics. Part B, Neuropsychiatric genetics : the official publication of the International Society of Psychiatric Genetics · 2025Pooled it
- Association Study and Meta-Analysis of Polymorphisms and Blood mRNA Expression of the ALDH2 Gene in Patients with Alzheimer's Disease.Journal of Alzheimer's disease : JAD · 2022Pooled it
- Genetic and epigenetic signatures associated with plasma oxytocin levels in children and adolescents with autism spectrum disorder.Autism research : official journal of the International Society for Autism Research · 2023Trial
- 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
- The evolution of AI-integrated genome editing and its challenges.Mammalian genome : official journal of the International Mammalian Genome Society · 2026Review
- Comparative Analysis of Deep Learning Techniques in Alzheimer's Disease Diagnosis: Trends, Challenges, and Future Directions.MethodsX · 2026Review
- A diagnostic plasma omics-biomarker for Alzheimer's disease informed by microglial single-cell transcriptomics: A pilot study.bioRxiv : the preprint server for biology · 2026Article
- Alzheimer's disease prediction using deep learning and XAI based interpretable feature selection from blood gene expression data.Scientific reports · 2026Article
- Small data, big challenges: Machine- and deep-learning strategies for data-limited drug discovery.Advanced drug delivery reviews · 2026Review
- Diagnosis of Alzheimer's disease with high accuracy via Petri net modeling of signaling pathways.Scientific reports · 2026Article
- AI-Guided Discovery of Oncogenic Signaling Crosstalk in Tumor Progression and Drug Resistance.Oncology research · 2026Review
- Hypothesis-Driven Insights and Clinical Trial Updates in Alzheimer's Disease Pathogenesis.CNS & neurological disorders drug targets · 2026Review
- HINN: Hierarchical Input Neural Network identifies multi-omics biomarker for cognitive decline.Research square · 2025Article
- Generative prediction of causal gene sets responsible for complex traits.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Developing non-invasive molecular markers for early risk assessment of Alzheimer's disease.Biomarkers in neuropsychiatry · 2025Article
- Deficits of Alzheimer's Disease Neuropsychological Architecture Correlate with Specific Exosomal mRNA Expression: Evidence of a Continuum?International journal of molecular sciences · 2025Article
- Machine Learning-Based Alzheimer's Disease Stage Diagnosis Utilizing Blood Gene Expression and Clinical Data: A Comparative Investigation.Diagnostics (Basel, Switzerland) · 2025Article
- A mini review of transforming dementia care in China with data-driven insights: overcoming diagnostic and time-delayed barriers.Frontiers in aging neuroscience · 2025Review
- Deep learning-based Alzheimer's disease detection using magnetic resonance imaging and gene expression data.PloS one · 2025Article
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Abstract
Identification of AD (Alzheimer's disease)-related genes obtained from blood samples is crucial for early AD diagnosis. We used three public datasets, ADNI, AddNeuroMed1 (ANM1), and ANM2, for this study. Five feature selection methods and five classifiers were used to curate AD-related genes and discriminate AD patients, respectively. In the internal validation (five-fold cross-validation within each dataset), the best average values of the area under the curve (AUC) were 0.657, 0.874, and 0.804 for ADNI, ANMI, and ANM2, respectively. In the external validation (training and test sets from different datasets), the best AUCs were 0.697 (training: ADNI to testing: ANM1), 0.764 (ADNI to ANM2), 0.619 (ANM1 to ADNI), 0.79 (ANM1 to ANM2), 0.655 (ANM2 to ADNI), and 0.859 (ANM2 to ANM1), respectively. These results suggest that although the classification performance of ADNI is relatively lower than that of ANM1 and ANM2, classifiers trained using blood gene expression can be used to classify AD for other data sets. In addition, pathway analysis showed that AD-related genes were enriched with inflammation, mitochondria, and Wnt signaling pathways. Our study suggests that blood gene expression data are useful in predicting the AD classification.
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