ArticleBMC genomics2024
DeepMoIC: multi-omics data integration via deep graph convolutional networks for cancer subtype classification.
Article in BMC genomics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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Who cites it
21 citing papers in PubMed.
- Multi-omics graph attention network reveals neuro-immune-tumor pathways in breast cancer liver depression syndrome.Frontiers in immunology · 2026Trial
- Review
- TF-DWGNet: a directed weighted graph neural network with tensor fusion for multi-omics cancer subtype classification.NAR genomics and bioinformatics · 2026Article
- AI and network biology for rational polypharmacology in signaling drug design: a review.NPJ precision oncology · 2026Review
- Graph designs for deep learning-based multi-omics integration.Briefings in bioinformatics · 2026Review
- Triad-LMF: a hierarchical low-rank multimodal fusion framework for robust cancer subtype classification using multi-omics data.BMC bioinformatics · 2026Article
- SeOMLR: one-step multi-view latent representation with self-weighted ensemble learning for multi-omics cancer subtyping.Bioinformatics (Oxford, England) · 2026Article
- Article
- Article
- Deep-learning-derived glaucoma-related endophenotypes enable novel genome-wide genetic and functional discovery.Research square · 2026Article
- Integrating multiomics data using a correlation based graph attention network for subtype classification in lower grade glioma.Discover oncology · 2026Article
- Clustering single-cell multi-omics data via multi-subspace contrastive learning with structural smoothness.Briefings in bioinformatics · 2026Article
- CancerSubtypeXplore: A Modular Platform for Multiomics Cancer Subtype Prediction and Biomarker Consensus Discovery.Computational and structural biotechnology journal · 2026Article
- Multi-omic data integration and exploiting metabolic models using systems biology approach increase precision in subtyping and early diagnosis of cancer.Quantitative biology (Beijing, China) · 2025Article
- Integrative Omics and AI-Driven Systems Biology: Multilayer Networks DecodingJournal of proteome research · 2025Review
- Deep-learning-derived glaucoma-related endophenotypes enable novel genome-wide genetic and functional discovery.medRxiv : the preprint server for health sciences · 2025Article
- Rank-based learning: a novel high-throughput algorithm resilient to missing data and effective for datasets with small sample size.Briefings in bioinformatics · 2025Article
- NeuroFANN: identification of neuropathological subtypes in dementia with plasma proteins by using functionally annotated neural network.Briefings in bioinformatics · 2025Article
- Decoding cancer prognosis with deep learning: the ASD-cancer framework for tumor microenvironment analysis.mSystems · 2025Article
- M-GNN: A Graph Neural Network Framework for Lung Cancer Detection Using Metabolomics and Heterogeneous Graph Modeling.International journal of molecular sciences · 2025Article
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6 authors.
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Abstract
backgroundAchieving precise cancer subtype classification is imperative for effective prognosis and treatment. Multi-omics studies, encompassing diverse data modalities, have emerged as powerful tools for unraveling the complexities of cancer. However, owing to the intricacies of biological data, multi-omics datasets generally show variations in data types, scales, and distributions. These intractable problems lead to challenges in exploring intact representations from heterogeneous data, which often result in inaccuracies in multi-omics information analysis.
resultsTo address the challenges of multi-omics research, our approach DeepMoIC presents a novel framework derived from deep Graph Convolutional Network (GCN). Leveraging autoencoder modules, DeepMoIC extracts compact representations from omics data and incorporates a patient similarity network through the similarity network fusion algorithm. To handle non-Euclidean data and explore high-order omics information effectively, we design a Deep GCN module with two strategies: residual connection and identity mapping. With extracted higher-order representations, our approach consistently outperforms state-of-the-art models on a pan-cancer dataset and 3 cancer subtype datasets.
conclusionThe introduction of Deep GCN shows encouraging performance in terms of supervised multi-omics feature learning, offering promising insights for precision medicine in cancer research. DeepMoIC can potentially be an important tool in the field of cancer subtype classification because of its capacity to handle complex multi-omics data and produce reliable classification findings.
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