ArticleNPJ systems biology and applications2025
Effective integration of multi-omics with prior knowledge to identify biomarkers via explainable graph neural networks.
Article in NPJ systems biology and applications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 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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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
20 citing papers in PubMed.
- Review
- Decoding neuronal gene expression: integrative insights from omics and AI.Brain informatics · 2026Review
- Protein glycoxidation in neuropsychiatric disorders-from basic research to clinical practice.Redox biology · 2026Review
- Artificial intelligence-driven multi-omics integration for plant enhancement: advances, challenges, and future perspectives.Functional & integrative genomics · 2026Review
- Mapping cross-domain drivers of Alzheimer's disease risk through integrated network analysis.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2026Article
- AI-driven big data analysis and predictive modeling of infectious disease immunity: from correlates to causal, multiscale understanding.Archives of microbiology · 2026Review
- Graph designs for deep learning-based multi-omics integration.Briefings in bioinformatics · 2026Review
- When complexity does not pay: benchmarking deep learning and ensemble methods for biomarker discovery.Briefings in bioinformatics · 2026Article
- Deep Learning-Enabled Multi-Omics Integration: A New Frontier in Precise Drug Target Discovery.Biology · 2026Review
- Uric acid-associated mechanisms of coronary artery calcification in diabetic kidney disease: evidence, hypotheses, and translational perspectives.Frontiers in cardiovascular medicine · 2026Review
- Intra- and inter-multi-omics interaction analysis using deep learning.Bioinformatics advances · 2026Article
- Integrative Analyses of Individual Patient Genomic-Data to Discover Novel Biomarkers: Application to Cervical Pre-Cancer DNA-Methylation Datasets.Cancer informatics · 2026Article
- Temporal network analysis in systems biology: concepts, inference, and validation.Frontiers in bioinformatics · 2026Review
- Systematic Review: Proteomics-Driven Multi-Omics Integration for Alzheimer's Disease Pathology and Precision Medicine.Neurology international · 2025Review
- Mapping cross-domain drivers of Alzheimer's disease risk through integrated network analysis.bioRxiv : the preprint server for biology · 2025Article
- Integrating artificial intelligence into small molecule development for precision cancer immunomodulation therapy.npj drug discovery · 2025Review
- Artificial intelligence-driven multi-omics approaches in Alzheimer's disease: Progress, challenges, and future directions.Acta pharmaceutica Sinica. B · 2025Review
- Review
- Biological determinants of blood-based biomarker levels in Alzheimer's disease: role of nutrition, inflammation, and metabolic factors.Frontiers in aging neuroscience · 2025Review
- Multimodal graph neural networks in healthcare: a review of fusion strategies across biomedical domains.Frontiers in artificial intelligence · 2025Review
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
The rapid growth of multi-omics datasets and the wealth of biological knowledge necessitates the development of effective methods for their integration. Such methods are essential for building predictive models and identifying drug targets based on a limited number of samples. We propose a framework called GNNRAI for the supervised integration of multi-omics data with biological priors represented as knowledge graphs. Our framework leverages graph neural networks (GNNs) to model the correlation structures among features from high-dimensional 'omics data, which reduces the effective dimensions in data and enables us to analyze thousands of genes simultaneously using hundreds of samples. Furthermore, our framework incorporates explainability methods to elucidate informative biomarkers. We apply our framework to Alzheimer's disease (AD) multi-omics data, showing that the integration of transcriptomics and proteomics data with prior AD knowledge is effective, improving the prediction accuracy of AD status over single-omics analyses and highlighting both known and novel AD-predictive biomarkers.
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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.