ArticleNAR genomics and bioinformatics2023
SUPREME: multiomics data integration using graph convolutional networks.
Article in NAR genomics and bioinformatics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
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Who cites it
22 citing papers in PubMed.
- The use of artificial intelligence in advancing molecular biology in Africa: a narrative review.Molecular genetics and genomics : MGG · 2026Review
- BioNeuralNet: a graph neural network based Multi-Omics network data analysis tool.Bioinformatics (Oxford, England) · 2026Article
- TF-DWGNet: a directed weighted graph neural network with tensor fusion for multi-omics cancer subtype classification.NAR genomics and bioinformatics · 2026Article
- Graph designs for deep learning-based multi-omics integration.Briefings in bioinformatics · 2026Review
- DyGraphTrans: A temporal graph representation learning framework for modeling disese progression from Electronic Health Records.bioRxiv : the preprint server for biology · 2026Article
- Metabolic-Epigenetic Crosstalk in Takayasu Arteritis: The ANK2-MAVS-IL-8 Axis as a Novel Therapeutic Paradigm.International journal of molecular sciences · 2026Review
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- Article
- MultiGEOmics: Graph-Based Integration of Multi-Omics via Biological Information Flows.bioRxiv : the preprint server for biology · 2026Article
- MoJKNet: a jumping knowledge graph framework for multi-omics cancer subtype prediction.Frontiers in genetics · 2026Article
- GCOA-Net: a graph-regularized cross-omics attention network for interpretable breast cancer molecular subtype classification.Frontiers in medicine · 2026Article
- SynOmics: integrating multi-omics data through feature interaction networks.Briefings in bioinformatics · 2025Article
- Uncovering the Understanding of the Concept of Patient Similarity in Cancer Research and Treatment: Scoping Review.Journal of medical Internet research · 2025Article
- MO-GCAN: multi-omics integration based on graph convolutional and attention networks.Bioinformatics (Oxford, England) · 2025Article
- DriverOmicsNet: an integrated graph convolutional network for multi-omics exploration of cancer driver genes.Briefings in bioinformatics · 2025Article
- IGCN: integrative graph convolution networks for patient level insights and biomarker discovery in multi-omics integration.Bioinformatics (Oxford, England) · 2025Article
- Fusing multiplex heterogeneous networks using graph attention-aware fusion networks.Scientific reports · 2024Article
- Supervised multiple kernel learning approaches for multi-omics data integration.BioData mining · 2024Article
- Graph machine learning for integrated multi-omics analysis.British journal of cancer · 2024Review
- MOGAT: A Multi-Omics Integration Framework Using Graph Attention Networks for Cancer Subtype Prediction.International journal of molecular sciences · 2024Article
Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
To pave the road towards precision medicine in cancer, patients with similar biology ought to be grouped into same cancer subtypes. Utilizing high-dimensional multiomics datasets, integrative approaches have been developed to uncover cancer subtypes. Recently, Graph Neural Networks have been discovered to learn node embeddings utilizing node features and associations on graph-structured data. Some integrative prediction tools have been developed leveraging these advances on multiple networks with some limitations. Addressing these limitations, we developed SUPREME, a node classification framework, which integrates multiple data modalities on graph-structured data. On breast cancer subtyping, unlike existing tools, SUPREME generates patient embeddings from multiple similarity networks utilizing multiomics features and integrates them with raw features to capture complementary signals. On breast cancer subtype prediction tasks from three datasets, SUPREME outperformed other tools. SUPREME-inferred subtypes had significant survival differences, mostly having more significance than ground truth, and outperformed nine other approaches. These results suggest that with proper multiomics data utilization, SUPREME could demystify undiscovered characteristics in cancer subtypes that cause significant survival differences and could improve ground truth label, which depends mainly on one datatype. In addition, to show model-agnostic property of SUPREME, we applied it to two additional datasets and had a clear outperformance.
Identifiers
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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.