ArticleScientific reports2022
On the limits of graph neural networks for the early diagnosis of Alzheimer's disease.
Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
14 citing papers in PubMed.
- Graph Neural Networks for Medical Imaging Analysis and Biological Data: Integrating Topology, Geometry, Radiomics, and Generative AI.Bioengineering (Basel, Switzerland) · 2026Review
- How Genomic and Structural Context Could Shape JAK-STAT Variant Pathogenicity.Twin research and human genetics : the official journal of the International Society for Twin Studies · 2026Article
- A physics-informed graph neural network to approximate docking-based binding affinity for DYRK2 in Alzheimer's drug repurposing.Scientific reports · 2026Article
- A Unified Framework for Alzheimer's Disease Knowledge Graphs: Architectures, Principles, and Clinical Translation.Brain sciences · 2025Review
- Data-driven projections of candidate enhancer-activating SNPs in immune regulation.BMC genomics · 2025Article
- Spotlight on amino acid changing mutations in the JAK-STAT pathway: from disease-specific mutation to general mutation databases.Scientific reports · 2025Article
- DEG-BRIN-GCN: interpretable graph convolutional framework with differentially expressed genes brain region interaction network prior for AD diagnosis.Frontiers in neuroscience · 2025Article
- Influence of multi-species data on gene-disease associations in substance use disorder using random walk with restart models.PloS one · 2025Article
- LIMO-GCN: a linear model-integrated graph convolutional network for predicting Alzheimer disease genes.Briefings in bioinformatics · 2024Article
- Network medicine-based epistasis detection in complex diseases: ready for quantum computing.Nucleic acids research · 2024Article
- Genetic-based patient stratification in Alzheimer's disease.Scientific reports · 2024Article
- Developing Explainable Deep Model for Discovering Novel Control Mechanism of Neuro-Dynamics.IEEE transactions on medical imaging · 2024Article
- Network medicine-based epistasis detection in complex diseases: ready for quantum computing.medRxiv : the preprint server for health sciences · 2023Article
- A review of multi-omics data integration through deep learning approaches for disease diagnosis, prognosis, and treatment.Frontiers in genetics · 2023Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
Alzheimer's disease (AD) is a neurodegenerative disease whose molecular mechanisms are activated several years before cognitive symptoms appear. Genotype-based prediction of the phenotype is thus a key challenge for the early diagnosis of AD. Machine learning techniques that have been proposed to address this challenge do not consider known biological interactions between the genes used as input features, thus neglecting important information about the disease mechanisms at play. To mitigate this, we first extracted AD subnetworks from several protein-protein interaction (PPI) databases and labeled these with genotype information (number of missense variants) to make them patient-specific. Next, we trained Graph Neural Networks (GNNs) on the patient-specific networks for phenotype prediction. We tested different PPI databases and compared the performance of the GNN models to baseline models using classical machine learning techniques, as well as randomized networks and input datasets. The overall results showed that GNNs could not outperform a baseline predictor only using the APOE gene, suggesting that missense variants are not sufficient to explain disease risk beyond the APOE status. Nevertheless, our results show that GNNs outperformed other machine learning techniques and that protein-protein interactions lead to superior results compared to randomized networks. These findings highlight that gene interactions are a valuable source of information in predicting disease status.
Indexed as
Identifiers
What Socratic holds
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.