ArticleFrontiers in genetics2022
MoGCN: A Multi-Omics Integration Method Based on Graph Convolutional Network for Cancer Subtype Analysis.
Article in Frontiers in genetics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 82 papers.
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82 citing papers in PubMed, 137 citations in OpenAlex.
- From Prediction to Mechanism: Explainable AI for Plant-Microbiome Multi-Omics Integration and Biological Discovery.International journal of molecular sciences · 2026Review
- In vitro processes alter the embryonic disc epigenome and transcriptome in the pre-implantation elongated bovine embryo†.Biology of reproduction · 2026Article
- FastMI-HGNet: A Two-Stream Heterogeneous Graph Neural Network for Multi-Omics Disease Classification.Genes · 2026Article
- MOCDT: multi-cancer detection and tissue-of-origin classification via cfDNA multi-modal integration.Bioinformatics (Oxford, England) · 2026Article
- Interpretable graph-based models on multimodal biomedical data integration: a technical review and benchmarking.Nature communications · 2026Review
- TF-DWGNet: a directed weighted graph neural network with tensor fusion for multi-omics cancer subtype classification.NAR genomics and bioinformatics · 2026Article
- DBCL-DFNet: Dual-Branch Contrastive Learning for Multi-Omics Dynamic Fusion.Entropy (Basel, Switzerland) · 2026Article
- Decoding disease and therapy through multiomics integration and systems analysis.Briefings in bioinformatics · 2026Review
- Graph designs for deep learning-based multi-omics integration.Briefings in bioinformatics · 2026Review
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- Triad-LMF: a hierarchical low-rank multimodal fusion framework for robust cancer subtype classification using multi-omics data.BMC bioinformatics · 2026Article
- Robust graph structure learning to improve multi-omics cancer subtype classification.BMC bioinformatics · 2026Article
- Graph Learning in Bioinformatics: A Survey of Graph Neural Network Architectures, Biological Graph Construction and Bioinformatics Applications.Biomolecules · 2026Review
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- AI and Big Data in Oncology: A Physician-Centered Perspective on Emerging Clinical and Research Applications.Cancer innovation · 2026Review
- MoAGNN: a multi-omics hierarchical graph neural network for subtype classification and prognosis prediction in lung adenocarcinoma.Briefings in bioinformatics · 2026Article
- AI-assisted multimodal data integration for precision oncology.Frontiers in artificial intelligence · 2026Review
- A prediction model for platinum-resistant recurrence of ovarian cancer was established using multimodal artificial intelligence machine learning methods.Frontiers in medicine · 2026Article
- An explainable-AI framework reveals novel lncRNAs specific for breast cancer subtypes.Frontiers in bioinformatics · 2026Article
22 more citing papers are in PubMed but not listed here.
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7 authors at 2 institutions in 1 country.
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No grant is acknowledged in the PubMed record.
Abstract
In light of the rapid accumulation of large-scale omics datasets, numerous studies have attempted to characterize the molecular and clinical features of cancers from a multi-omics perspective. However, there are great challenges in integrating multi-omics using machine learning methods for cancer subtype classification. In this study, MoGCN, a multi-omics integration model based on graph convolutional network (GCN) was developed for cancer subtype classification and analysis. Genomics, transcriptomics and proteomics datasets for 511 breast invasive carcinoma (BRCA) samples were downloaded from the Cancer Genome Atlas (TCGA). The autoencoder (AE) and the similarity network fusion (SNF) methods were used to reduce dimensionality and construct the patient similarity network (PSN), respectively. Then the vector features and the PSN were input into the GCN for training and testing. Feature extraction and network visualization were used for further biological knowledge discovery and subtype classification. In the analysis of multi-dimensional omics data of the BRCA samples in TCGA, MoGCN achieved the highest accuracy in cancer subtype classification compared with several popular algorithms. Moreover, MoGCN can extract the most significant features of each omics layer and provide candidate functional molecules for further analysis of their biological effects. And network visualization showed that MoGCN could make clinically intuitive diagnosis. The generality of MoGCN was proven on the TCGA pan-kidney cancer datasets. MoGCN and datasets are public available at https://github.com/Lifoof/MoGCN. Our study shows that MoGCN performs well for heterogeneous data integration and the interpretability of classification results, which confers great potential for applications in biomarker identification and clinical diagnosis.
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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.