ArticleNature communications2021
MOGONET integrates multi-omics data using graph convolutional networks allowing patient classification and biomarker identification.
Article in Nature communications, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 295 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
295 citing papers in PubMed.
- Multi-omics graph attention network reveals neuro-immune-tumor pathways in breast cancer liver depression syndrome.Frontiers in immunology · 2026Trial
- AI-based multimodal integration of genomics and electronic health records.Nature reviews. Genetics · 2026Review
- Multi-omics integration of proteomics and metabolomics in pediatric health and disease.Communications medicine · 2026Review
- TMO-Net+: An Enhanced Tumor Multi-Omics Pre-Trained Network for Multi-Task Learning in Oncology.Genes · 2026Article
- BOMIFA: biologically informed multi-omics integration with graph contrastive learning for cancer prognosis in women.Briefings in bioinformatics · 2026Article
- Next-generation kidney tissue analysis - spatial omics and digital pathology.Nature reviews. Nephrology · 2026Review
- Genome-phenome association prediction using weighted deep matrix factorization with a multisource graph attention network.Quantitative biology (Beijing, China) · 2026Article
- Integration of artificial intelligence and multi-omics for precision medicine.Functional & integrative genomics · 2026Review
- CCNA2 orchestrates the PI3K/AKT signaling axis to propel prostate cancer metastasis.Translational andrology and urology · 2026Article
- Multi-omics-driven precision medicine.iMeta · 2026Review
- Artificial intelligence in oncology: linking biological discovery to clinical utility.Molecular cancer · 2026Review
- Consistency of feature attribution in deep learning architectures for multi-omics.Scientific reports · 2026Article
- Biochemically Constrained Multi-Omics Integration Reveals Protein-Metabolite Dependencies Across Diseases.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Integrative Epigenomics: Bioinformatics Strategies for Multi-Omics Data Analysis in Health and Disease.Epigenomes · 2026Review
- Microbiome and aging: Trajectories of microbiome age across human ecosystems and their systemic effects.iMeta · 2026Review
- Mapping the path to clinical implementation of multi-omics.Nature genetics · 2026Review
- Causal graph neural networks for healthcare.Nature biomedical engineering · 2026Review
- CAECC-Subtyper: A Novel Convolutional Autoencoder Framework for Integrating Multi-omics Data in Cancer Subtyping.Biochemical genetics · 2026Article
- FastMI-HGNet: A Two-Stream Heterogeneous Graph Neural Network for Multi-Omics Disease Classification.Genes · 2026Article
- EMMA-STRAT: a multi-omics based machine learning framework for stratification of endometrial carcinoma molecular subtypes and MSI status.BioData mining · 2026Article
235 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
Abstract
To fully utilize the advances in omics technologies and achieve a more comprehensive understanding of human diseases, novel computational methods are required for integrative analysis of multiple types of omics data. Here, we present a novel multi-omics integrative method named Multi-Omics Graph cOnvolutional NETworks (MOGONET) for biomedical classification. MOGONET jointly explores omics-specific learning and cross-omics correlation learning for effective multi-omics data classification. We demonstrate that MOGONET outperforms other state-of-the-art supervised multi-omics integrative analysis approaches from different biomedical classification applications using mRNA expression data, DNA methylation data, and microRNA expression data. Furthermore, MOGONET can identify important biomarkers from different omics data types related to the investigated biomedical problems.
Indexed as
Identifiers
What OpenQuestion holds
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.