ArticleScientific reports2024
Predicting early Alzheimer's with blood biomarkers and clinical features.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 24 papers.
What it found
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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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
24 citing papers in PubMed, 42 citations in OpenAlex.
- Machine Learning and Multimodal Biomarker Discovery in Alzheimer's Disease.Brain sciences · 2026Review
- Discovery-Driven Plasma Proteomics Identifies a Multi-Protein Signature for Amyloid PET Positivity: A Machine Learning Analysis of the Bio-Hermes Cohort.International journal of molecular sciences · 2026Article
- DAG-VAERL: a novel causal inference method for building causal gene regulatory networks.BioData mining · 2026Article
- Profiling of extracellular vesicles from primary hepatocytes, organoids, and mash patients identifies cell injury-specific signatures.Scientific reports · 2026Article
- Review
- RNA-seq variants reveal distinct patterns in the aging epitranscriptome: an in-depth analysis of age-matched Alzheimer's Disease patients and a cognitively normal cohort.bioRxiv : the preprint server for biology · 2026Article
- Diagnosis of Alzheimer's disease with high accuracy via Petri net modeling of signaling pathways.Scientific reports · 2026Article
- Use of explainable AI (xAI) in dementia detection and prognosis: a scoping review.BMC medical informatics and decision making · 2026Article
- Screen, Sample, Stratify: Biomarkers and Machine Learning Compress Dementia Pathways.Biomedicines · 2026Article
- A Scoping Review of Machine Learning-Based Prediction of Alzheimer's Disease Using Blood Biomarkers.Biomedical engineering and computational biology · 2026Review
- Identification of age-specific urinary metabolic biomarkers in Wilson disease using machine learning: a comparative study of ensemble tree models.Open medicine (Warsaw, Poland) · 2026Article
- Prediction of Episodic Memory With Multiomics Scores.Biological psychiatry global open science · 2026Article
- Explainable hierarchical machine-learning approaches for multimodal prediction of conversion from mild cognitive impairment to Alzheimer's disease.Physical and engineering sciences in medicine · 2025Article
- Cognitive impairment and p-tau217 are high in a vascular patient cohort.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2025Article
- Visualizing functional network connectivity differences using an explainable machine-learning method.Physiological measurement · 2025Article
- PPIxGPN: plasma proteomic profiling of neurodegenerative biomarkers with protein-protein interaction-based eXplainable graph propagational network.Briefings in bioinformatics · 2025Article
- Machine learning to detect Alzheimer's disease with data on drugs and diagnoses.The journal of prevention of Alzheimer's disease · 2025Article
- Alzheimer's Disease: Recent Developments in Pathogenesis, Diagnosis, and Therapy.Life (Basel, Switzerland) · 2025Article
- Biomarkers of blood-brain barrier and neurovascular unit integrity in human cognitive impairment and dementia.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2025Review
- Machine Learning-Based Alzheimer's Disease Stage Diagnosis Utilizing Blood Gene Expression and Clinical Data: A Comparative Investigation.Diagnostics (Basel, Switzerland) · 2025Article
Corrections and comments
- Erratum issued
Authors and funding
4 authors at 1 institution in 1 country.
Funding
Abstract
Alzheimer's disease (AD) is an incurable neurodegenerative disorder that leads to dementia. This study employs explainable machine learning models to detect dementia cases using blood gene expression, single nucleotide polymorphisms (SNPs), and clinical data from Alzheimer's Disease Neuroimaging Initiative (ADNI). Analyzing 623 ADNI participants, we found that the Support Vector Machine classifier with Mutual Information (MI) feature selection, trained on all three data modalities, achieved exceptional performance (accuracy = 0.95, AUC = 0.94). When using gene expression and SNP data separately, we achieved very good performance (AUC = 0.65, AUC = 0.63, respectively). Using SHapley Additive exPlanations (SHAP), we identified significant features, potentially serving as AD biomarkers. Notably, genetic-based biomarkers linked to axon myelination and synaptic vesicle membrane formation could aid early AD detection. In summary, this genetic-based biomarker approach, integrating machine learning and SHAP, shows promise for precise AD diagnosis, biomarker discovery, and offers novel insights for understanding and treating the disease. This approach addresses the challenges of accurate AD diagnosis, which is crucial given the complexities associated with the disease and the need for non-invasive diagnostic methods.
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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.