ArticleBriefings in bioinformatics2024
Multi-view multi-level contrastive graph convolutional network for cancer subtyping on multi-omics data.
Article in Briefings in bioinformatics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Machine learning and AI for cancer research and care: a review of applications, limitations, and future directions.Journal of the Egyptian National Cancer Institute · 2026Review
- A Bioinformatics Analysis Based on Omics and Clinical Data for Graph-Based Patient Stratification in Hepatocellular Carcinoma.Biology · 2026Article
- OmicsTransformer: self-supervised masked consistency and uncertainty-aware fusion for robust multi-omics prediction.Bioinformatics (Oxford, England) · 2026Article
- Graph designs for deep learning-based multi-omics integration.Briefings in bioinformatics · 2026Review
- SeOMLR: one-step multi-view latent representation with self-weighted ensemble learning for multi-omics cancer subtyping.Bioinformatics (Oxford, England) · 2026Article
- Robust graph structure learning to improve multi-omics cancer subtype classification.BMC bioinformatics · 2026Article
- AI-driven virtual cell models in preclinical research: technical pathways, validation mechanisms, and clinical translation potential.NPJ digital medicine · 2025Review
- Decoupled contrastive multi-view clustering with adaptive false negative elimination for cancer subtyping.PLoS computational biology · 2025Article
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Authors and funding
5 authors.
Funding
Abstract
Cancer is a highly diverse group of diseases, and each type of cancer can be further divided into various subtypes according to specific characteristics, cellular origins, and molecular markers. Subtyping helps in tailoring treatment and prognosis accuracy. However, the existing studies are more concerned with integrating different omics data to discover potential connections, but ignoring the relationships between consensus information and individual information within each omics level during the integration process. To this end, we propose a novel fusion-free method called multi-view multi-level contrastive graph convolutional network (M$^{2}$CGCN) for cancer subtyping. M$^{2}$CGCN learns multi-level features, i.e. high-level and low-level features, respectively. The low-level features from each view capture the intrinsic information in each omics by reconstruction of node attribute and graph structures. The high-level features achieve cancer subtyping via contrastive learning. Comprehensive experiments were performed on 34 multi-omics cancer datasets. The findings indicate that M$^{2}$CGCN achieves results comparable to or surpassing many state-of-the-art methods.
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Registered trials
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