ArticleiScience2025
M3NetFlow: A multi-scale multi-hop graph AI model for integrative multi-omic data analysis.
Article in iScience, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 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
11 citing papers in PubMed.
- Towards Structural Restoration: Epigenetic Reprogramming and Direct Astrocyte-to-Neuron Lineage Conversion as Next-Generation Regenerative Neurotherapeutics.Molecular neurobiology · 2026Review
- Graph in Graph (GiG): A novel graph AI framework for integrating and interpreting medical and omics data.bioRxiv : the preprint server for biology · 2026Article
- Artificial intelligence construction: a review of the bridge between CT imaging features of lung ground-glass nodules adenocarcinoma and carcinogenic driver genes.Journal of cancer research and clinical oncology · 2026Review
- Mechanism-based prediction of drug synergy via network controllability analysis of therapeutic pathways in intractable diseases.iScience · 2026Article
- OmniCellTOSG: The First Cell Text-Omic Signaling Graphs Dataset for Graph Language Foundation Modeling.Research square · 2026Article
- BioMedGraphica: An All-in-One Platform for Joint Textual Biomedical Prior Knowledge and Numeric Graph Generation.bioRxiv : the preprint server for biology · 2025Article
- Artificial intelligence-driven multi-omics approaches in Alzheimer's disease: Progress, challenges, and future directions.Acta pharmaceutica Sinica. B · 2025Review
- OmniCellAgent: Towards AI Co-Scientists for Scientific Discovery in Precision Medicine.bioRxiv : the preprint server for biology · 2025Article
- mosGraphGen: a novel tool to generate multi-omics signaling graphs to facilitate integrative and interpretable graph AI model development.bioRxiv : the preprint server for biology · 2024Article
- mosGraphGPT: a foundation model for multi-omic signaling graphs using generative AI.bioRxiv : the preprint server for biology · 2024Article
- AI for Scientific Discovery in Omics Data-Driven Precision Medicine.Missouri medicineArticle
Corrections and comments
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Authors and funding
9 authors.
Funding
Abstract
Multi-omic data-driven studies are at the forefront of precision medicine by characterizing complex disease signaling systems across multiple views and levels. The integration and interpretation of multi-omic data are critical for identifying disease targets and deciphering disease signaling pathways. However, it remains an open problem due to the complex signaling interactions among many proteins. Herein, we propose a multi-scale multi-hop multi-omic network flow model, M3NetFlow, to facilitate both hypothesis-guided and generic multi-omic data analysis tasks. We evaluated M3NetFlow using two independent case studies: (1) uncovering mechanisms of synergy of drug combinations (hypothesis/anchor-target guided multi-omic analysis) and (2) identifying biomarkers of Alzheimer's disease (generic multi-omic analysis). The evaluation and comparison results showed that M3NetFlow achieved the best prediction accuracy and identified a set of drug combination synergy- and disease-associated targets. The model can be directly applied to other multi-omic data-driven studies.
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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.