ArticleBioinformatics (Oxford, England)2023
MRGCN: cancer subtyping with multi-reconstruction graph convolutional network using full and partial multi-omics dataset.
Article in Bioinformatics (Oxford, England), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 1 of them a synthesis that pooled it.
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14 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Application of multi-omics in systemic autoimmune rheumatic diseases: a bibliometric and visualization analysis.Frontiers in immunology · 2026Pooled it
- Multi-modal learning with incomplete data.Nature communications · 2026Article
- Article
- Graph designs for deep learning-based multi-omics integration.Briefings in bioinformatics · 2026Review
- CSIE: cancer subtyping via inference and ensemble.Briefings in bioinformatics · 2026Article
- Discovering proteo-transcriptomic networks via biologically informed heterogeneous graph learning.Nucleic acids research · 2026Article
- Decoupled contrastive multi-view clustering with adaptive false negative elimination for cancer subtyping.PLoS computational biology · 2025Article
- DSCC: disease subtyping using spectral clustering and community detection from consensus networks.Briefings in bioinformatics · 2025Article
- DeepMoIC: multi-omics data integration via deep graph convolutional networks for cancer subtype classification.BMC genomics · 2024Article
- MoAGL-SA: a multi-omics adaptive integration method with graph learning and self attention for cancer subtype classification.BMC bioinformatics · 2024Article
- IPFMC: an iterative pathway fusion approach for enhanced multi-omics clustering in cancer research.Briefings in bioinformatics · 2024Article
- Deeply integrating latent consistent representations in high-noise multi-omics data for cancer subtyping.Briefings in bioinformatics · 2024Article
- Multi-omics integration with weighted affinity and self-diffusion applied for cancer subtypes identification.Journal of translational medicine · 2024Article
- Exploring the Application of the Artificial-Intelligence-Integrated Platform 3D Slicer in Medical Imaging Education.Diagnostics (Basel, Switzerland) · 2024Review
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4 authors.
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Abstract
motivationCancer is a molecular complex and heterogeneous disease. Each type of cancer is usually composed of several subtypes with different treatment responses and clinical outcomes. Therefore, subtyping is a crucial step in cancer diagnosis and therapy. The rapid advances in high-throughput sequencing technologies provide an increasing amount of multi-omics data, which benefits our understanding of cancer genetic architecture, and yet poses new challenges in multi-omics data integration.
resultsWe propose a graph convolutional network model, called MRGCN for multi-omics data integrative representation. MRGCN simultaneously encodes and reconstructs multiple omics expression and similarity relationships into a shared latent embedding space. In addition, MRGCN adopts an indicator matrix to denote the situation of missing values in partial omics, so that the full and partial multi-omics processing procedures are combined in a unified framework. Experimental results on 11 multi-omics datasets show that cancer subtypes obtained by MRGCN with superior enriched clinical parameters and log-rank test P-values in survival analysis over many typical integrative methods. AVAILABILITY AND IMPLEMENTATION: https://github.com/Polytech-bioinf/MRGCN.git https://figshare.com/articles/software/MRGCN/23058503.
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