ArticleBioinformatics (Oxford, England)2025
DGHNN: a deep graph and hypergraph neural network for pan-cancer related gene prediction.
Article in Bioinformatics (Oxford, England), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- MSF-HierGNN: a multi-source substructure-fusion hierarchical GNN method and web server to predict molecular property for drug design.Briefings in bioinformatics · 2026Article
- CryoPromptSeg: prompt-guided segmentation with integrated denoising for cryo-EM particle picking.Bioinformatics (Oxford, England) · 2026Article
- Applications of large-scale artificial intelligence models in bioinformatics.Quantitative biology (Beijing, China) · 2026Review
- Graph and Hypergraph Theories Applied to Dynamic Protein-Protein Interaction Network Analysis, and Deep-Learning Frameworks for Protein Complex Network Prediction.International journal of molecular sciences · 2026Review
- A data privacy protection method for infectious disease prediction models with balanced training speed and accuracy.Scientific reports · 2026Article
- IFNIKB: a type I interferon database for antitumuor immunity studies.Database : the journal of biological databases and curation · 2026Article
- Article
- Computational models for pan-cancer classification based on multi-omics data.Frontiers in genetics · 2025Review
- Developing a quantum computing model for sequence annotation of interferon protein.Computational and structural biotechnology journal · 2025Article
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Authors and funding
5 authors.
Funding
Abstract
motivationStudies on pan-cancer related genes play important roles in cancer research and precision therapy. With the richness of research data and the development of neural networks, several successful methods that take advantage of multiomics data, protein interaction networks, and graph neural networks to predict cancer genes have emerged. However, these methods also have several problems, such as ignoring potentially useful biological data and providing limited representations of higher-order information.
resultsIn this work, we propose a pan-cancer related gene predictive model, the DGHNN, which takes biological pathways into consideration, applies a deep graph and hypergraph neural network to encode the higher-order information in the protein interaction network and biological pathway, introduces skip residual connections into the deep graph and hypergraph neural network to avoid problems with training the deep neural network, and finally uses a feature tokenizer and transformer for classification. The experimental results show that the DGHNN outperforms other methods and achieves state-of-the-art model performance for pan-cancer related gene prediction. AVAILABILITY AND IMPLEMENTATION: The DGHNN is available at https://github.com/skytea/DGHNN.
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