HyperNetWalk: A Unified Framework for Personalized and Population-Level Cancer Driver Gene Identification via Multi-Network Hypergraph Diffusion
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arXiv
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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| _version_ | 1866917183092686848 |
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| author | Xu, Xueqing Gao, Yonghang Sun, Duanchen Wu, Ling-Yun |
| author_facet | Xu, Xueqing Gao, Yonghang Sun, Duanchen Wu, Ling-Yun |
| contents | Identifying cancer driver genes is crucial for understanding tumor biology and developing precision therapies. However, existing computational methods often rely on single biological networks or population-level mutation patterns, limiting their ability to identify patient-specific drivers and leverage the complementary information from multiple network types. Here, we present HyperNetWalk, a novel computational framework that integrates multiple biological networks and hypergraph diffusion to identify driver genes at both personalized and cohort levels. In the first stage, HyperNetWalk integrates protein-protein interaction networks, gene regulatory networks, and dynamic co-expression networks through sample-independent random walks on patient-specific subnetworks to capture topological importance and expression perturbation effects. In the second stage, it refines predictions through hypergraph-based random walks that leverage cross-sample information while preserving individual mutational contexts. Comprehensive evaluation on 12 TCGA cancer types demonstrates that HyperNetWalk achieves superior or competitive performance compared to state-of-the-art methods in both personalized and cohort-level predictions. Notably, HyperNetWalk successfully identifies known driver genes with high precision while revealing cancer type-specific drivers that reflect distinct biological mechanisms. Our framework provides a unified solution for personalized and population-based driver gene identification, offering valuable insights for precision oncology and therapeutic target discovery. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_01337 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | HyperNetWalk: A Unified Framework for Personalized and Population-Level Cancer Driver Gene Identification via Multi-Network Hypergraph Diffusion Xu, Xueqing Gao, Yonghang Sun, Duanchen Wu, Ling-Yun Quantitative Methods Molecular Networks 92B05 J.3 Identifying cancer driver genes is crucial for understanding tumor biology and developing precision therapies. However, existing computational methods often rely on single biological networks or population-level mutation patterns, limiting their ability to identify patient-specific drivers and leverage the complementary information from multiple network types. Here, we present HyperNetWalk, a novel computational framework that integrates multiple biological networks and hypergraph diffusion to identify driver genes at both personalized and cohort levels. In the first stage, HyperNetWalk integrates protein-protein interaction networks, gene regulatory networks, and dynamic co-expression networks through sample-independent random walks on patient-specific subnetworks to capture topological importance and expression perturbation effects. In the second stage, it refines predictions through hypergraph-based random walks that leverage cross-sample information while preserving individual mutational contexts. Comprehensive evaluation on 12 TCGA cancer types demonstrates that HyperNetWalk achieves superior or competitive performance compared to state-of-the-art methods in both personalized and cohort-level predictions. Notably, HyperNetWalk successfully identifies known driver genes with high precision while revealing cancer type-specific drivers that reflect distinct biological mechanisms. Our framework provides a unified solution for personalized and population-based driver gene identification, offering valuable insights for precision oncology and therapeutic target discovery. |
| title | HyperNetWalk: A Unified Framework for Personalized and Population-Level Cancer Driver Gene Identification via Multi-Network Hypergraph Diffusion |
| topic | Quantitative Methods Molecular Networks 92B05 J.3 |
| url | https://arxiv.org/abs/2601.01337 |