ArticleBMC bioinformatics2022
Risk stratification and pathway analysis based on graph neural network and interpretable algorithm.
Article in BMC bioinformatics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed.
- Sparsity is all you need: rethinking biologically informed neural networks.Briefings in bioinformatics · 2026Article
- Causality-aware graph neural networks for functional stratification and phenotype prediction at scale.NPJ systems biology and applications · 2025Article
- AI-powered precision medicine: utilizing genetic risk factor optimization to revolutionize healthcare.NAR genomics and bioinformatics · 2025Review
- Article
- Amogel: a multi-omics classification framework using associative graph neural networks with prior knowledge for biomarker identification.BMC bioinformatics · 2025Article
- Strategies to include prior knowledge in omics analysis with deep neural networks.Patterns (New York, N.Y.) · 2025Review
- Article
- Pathway-guided architectures for interpretable AI in biological research.Computational and structural biotechnology journal · 2025Review
- Integration of Graph Neural Networks and multi-omics analysis identify the predictive factor and key gene for immunotherapy response and prognosis of bladder cancer.Journal of translational medicine · 2024Article
- Designing interpretable deep learning applications for functional genomics: a quantitative analysis.Briefings in bioinformatics · 2024Review
- Prior knowledge-guided multilevel graph neural network for tumor risk prediction and interpretation via multi-omics data integration.Briefings in bioinformatics · 2024Article
- KNeXT: a NetworkX-based topologically relevant KEGG parser.Frontiers in genetics · 2024Article
- Review
- Unbiased curriculum learning enhanced global-local graph neural network for protein thermodynamic stability prediction.Bioinformatics (Oxford, England) · 2023Article
- GNN-surv: Discrete-Time Survival Prediction Using Graph Neural Networks.Bioengineering (Basel, Switzerland) · 2023Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundPathway-based analysis of transcriptomic data has shown greater stability and better performance than traditional gene-based analysis. Until now, some pathway-based deep learning models have been developed for bioinformatic analysis, but these models have not fully considered the topological features of pathways, which limits the performance of the final prediction result.
resultsTo address this issue, we propose a novel model, called PathGNN, which constructs a Graph Neural Networks (GNNs) model that can capture topological features of pathways. As a case, PathGNN was applied to predict long-term survival of four types of cancer and achieved promising predictive performance when compared to other common methods. Furthermore, the adoption of an interpretation algorithm enabled the identification of plausible pathways associated with survival.
conclusionPathGNN demonstrates that GNN can be effectively applied to build a pathway-based model, resulting in promising predictive power.
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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.