ArticleNature biomedical engineering2025
Interpretable identification of cancer genes across biological networks via transformer-powered graph representation learning.
Article in Nature biomedical engineering, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 52 papers.
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Who cites it
52 citing papers in PubMed.
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- AET5: A transcriptome-guided molecular generation framework with contrastive self-supervised learning.PLoS computational biology · 2026Article
- Uncertainty-aware graph structure optimization with ensemble learning for enhanced cancer gene identification.Cell reports methods · 2026Article
- Edge-aware GAT-based protein binding sites prediction.PLoS computational biology · 2026Article
- Advancing AI for multi-omics and clinical data integration in basic and translational cancer research.Nature reviews. Cancer · 2026Review
- Computational prediction of TCR cross-reactivity: principles, challenges and translational opportunities.Journal of translational medicine · 2026Review
- Towards the expressive power of translating approach for knowledge graph completion.Scientific reports · 2026Article
- A multimodal human protein embeddings database: DeepDrug Protein Embeddings Bank (DPEB).NAR genomics and bioinformatics · 2026Article
- Hierarchical graph attention knowledge tracing and personalized recommendation for computer courses.Scientific reports · 2026Article
- Intelligent audit decision support for enterprise related-party transactions based on knowledge graph and graph neural network.Scientific reports · 2026Article
- Artificial intelligence integrated multi-omics and multimodal studies promote the efficacy of neoadjuvant chemotherapy in breast cancer: opportunities, challenges, and future perspectives.Breast cancer research : BCR · 2026Review
- SSAS-GO: structure-sequence adaptive synergy network for protein function prediction.Briefings in bioinformatics · 2026Article
- A prototype-augmented graph representation learning framework for identifying brain disorder-associated genes and facilitating drug repurposing.PLoS computational biology · 2026Article
- Multilabel prediction of virus target proteins via multimodal graph representation learning.PLoS computational biology · 2026Article
- UGP system: A deep learning-driven platform for automated identification of ultrafine granular powders using chromatographic fingerprinting.Journal of pharmaceutical analysis · 2026Article
- A multi-task heterogeneous graph learning with cross-attention fusion for robust recommendation.Scientific reports · 2026Article
- Multiscale-Aware Graph Embedding Approach Uncovers LC-61, a Potent Anti-Journal of chemical information and modeling · 2026Article
- Hybrid tuned deep learning model for breast cancer diagnosis using genetic data.Scientific reports · 2026Article
- Integrated spatio-temporal modeling with hybrid graph convolutions and the graph fourier neural operator for traffic prediction.Scientific reports · 2026Article
- Computational framework for therapeutic target discovery via perturbation simulation: application to cystic fibrosis airway disease.Briefings in bioinformatics · 2026Article
Corrections and comments
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Authors and funding
8 authors.
Funding
Abstract
Graph representation learning has been leveraged to identify cancer genes from biological networks. However, its applicability is limited by insufficient interpretability and generalizability under integrative network analysis. Here we report the development of an interpretable and generalizable transformer-based model that accurately predicts cancer genes by leveraging graph representation learning and the integration of multi-omics data with the topologies of homogeneous and heterogeneous networks of biological interactions. The model allows for the interpretation of the respective importance of multi-omic and higher-order structural features, achieved state-of-the-art performance in the prediction of cancer genes across biological networks (including networks of interactions between miRNA and proteins, transcription factors and proteins, and transcription factors and miRNA) in pan-cancer and cancer-specific scenarios, and predicted 57 cancer-gene candidates (including three genes that had not been identified by other models) among 4,729 unlabelled genes across 8 pan-cancer datasets. The model's interpretability and generalization may facilitate the understanding of gene-related regulatory mechanisms and the discovery of new cancer genes.
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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.