ArticleBriefings in bioinformatics2024
Prior knowledge-guided multilevel graph neural network for tumor risk prediction and interpretation via multi-omics data integration.
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 30 papers, 2 of them syntheses that pooled it.
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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.
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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.
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Who cites it
30 citing papers in PubMed, 2 syntheses or guidelines pooled it, 27 citations in OpenAlex.
- Deep learning in single-cell and spatial transcriptomics data analysis: advances and challenges from a data science perspective.Briefings in bioinformatics · 2025Pooled it
- Visible neural networks for multi-omics integration: a critical review.Frontiers in artificial intelligence · 2025Pooled it
- BOMIFA: biologically informed multi-omics integration with graph contrastive learning for cancer prognosis in women.Briefings in bioinformatics · 2026Article
- Toward A Pre-disease State-centered New Paradigm in Multi-omics Research.Genomics, proteomics & bioinformatics · 2026Article
- Sparsity is all you need: rethinking biologically informed neural networks.Briefings in bioinformatics · 2026Article
- PEARL: integrative multi-omics classification and omics feature discovery via deep graph learning.Bioinformatics (Oxford, England) · 2026Article
- Deep chemical structure graph learning deciphers the lipotoxicity code of hypertriglyceridemic pancreatitis.NPJ digital medicine · 2026Article
- Integrating AI in seed science: Toward an intelligent design paradigm.Plant communications · 2026Review
- Toward trustworthy artificial intelligence in multi-omics: a review of reproducibility, stability, and interpretability.Briefings in bioinformatics · 2026Review
- Graph designs for deep learning-based multi-omics integration.Briefings in bioinformatics · 2026Review
- PVAED: prior-guided variational autoencoders with diffusion denoising for interpretable single-cell representation learning.Briefings in bioinformatics · 2026Article
- M[Formula: see text]DGAT: Multi-view multi-scale dynamic graph attention network(GAT) based prediction of Parkinson's disease(PD) progression using whole-blood RNA sequencing data.Scientific reports · 2026Article
- Ferroptosis as a Novel Therapeutic Strategy to Overcome Multidrug Resistance in Colorectal Cancer.Pharmaceuticals (Basel, Switzerland) · 2026Review
- MoAGNN: a multi-omics hierarchical graph neural network for subtype classification and prognosis prediction in lung adenocarcinoma.Briefings in bioinformatics · 2026Article
- Intra- and inter-multi-omics interaction analysis using deep learning.Bioinformatics advances · 2026Article
- RAG-GNN: retrieval-augmented graph neural networks for protein interaction network embeddings.Frontiers in artificial intelligence · 2026Article
- Machine learning for drug-target interaction prediction: A comprehensive review of models, challenges, and computational strategies.Computational and structural biotechnology journal · 2026Review
- Graph neural networks for multi-scale gene-drug interaction modeling in cancer: applications, challenges, and therapeutic opportunities for breast cancer drug resistance.Frontiers in oncology · 2026Review
- Temporal network analysis in systems biology: concepts, inference, and validation.Frontiers in bioinformatics · 2026Review
- MOGEDN: small-sample cancer subtype classification with encoder-decoder networks for missing-omics recovery and biomarker discovery.Briefings in bioinformatics · 2025Article
Corrections and comments
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Authors and funding
6 authors at 3 institutions in 1 country.
Funding
Abstract
The interrelation and complementary nature of multi-omics data can provide valuable insights into the intricate molecular mechanisms underlying diseases. However, challenges such as limited sample size, high data dimensionality and differences in omics modalities pose significant obstacles to fully harnessing the potential of these data. The prior knowledge such as gene regulatory network and pathway information harbors useful gene-gene interaction and gene functional module information. To effectively integrate multi-omics data and make full use of the prior knowledge, here, we propose a Multilevel-graph neural network (GNN): a hierarchically designed deep learning algorithm that sequentially leverages multi-omics data, gene regulatory networks and pathway information to extract features and enhance accuracy in predicting survival risk. Our method achieved better accuracy compared with existing methods. Furthermore, key factors nonlinearly associated with the tumor pathogenesis are prioritized by employing two interpretation algorithms (i.e. GNN-Explainer and IGscore) for neural networks, at gene and pathway level, respectively. The top genes and pathways exhibit strong associations with disease in survival analyses, many of which such as SEC61G and CYP27B1 are previously reported in the literature.
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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.