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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2605.11450 |
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| _version_ | 1866918496121651200 |
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| author | Viana, Edmara Ramos, Rodrigo Henrique Carneiro, Flávia Raquel Gonçalves Ferreira, Cynthia de Oliveira Lage |
| author_facet | Viana, Edmara Ramos, Rodrigo Henrique Carneiro, Flávia Raquel Gonçalves Ferreira, Cynthia de Oliveira Lage |
| contents | Topological data analysis (TDA) has established itself as a useful tool for capturing multiscale structures in complex networks, such as connected components, cycles, and cavities. Although Vietoris-Rips (VR) filtering is widely used in network analysis, it tends to be computationally expensive, especially for large networks. This work explores vertex function-based (VFB) filtering based on network measures, applying persistent homology to identify relevant topological structures in cancer-associated protein networks, and compares its effectiveness with the VR approach. The results show that VFB reproduces the second-order structures (Betti-2) identified by VR, recovering previously reported essential genes. In addition, VFB detected new driver genes, confirmed in databases such as IntOGen and NCG, and allowed analysis of third-order structures (Betti-3) that was not feasible with VR. Thus, VFB represents a scalable alternative to VR, preserving biological interpretability and complementing classical network metrics. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_11450 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Scalable vertex guided filtrations identify structurally relevant genes in cancer networks Viana, Edmara Ramos, Rodrigo Henrique Carneiro, Flávia Raquel Gonçalves Ferreira, Cynthia de Oliveira Lage Molecular Networks Topological data analysis (TDA) has established itself as a useful tool for capturing multiscale structures in complex networks, such as connected components, cycles, and cavities. Although Vietoris-Rips (VR) filtering is widely used in network analysis, it tends to be computationally expensive, especially for large networks. This work explores vertex function-based (VFB) filtering based on network measures, applying persistent homology to identify relevant topological structures in cancer-associated protein networks, and compares its effectiveness with the VR approach. The results show that VFB reproduces the second-order structures (Betti-2) identified by VR, recovering previously reported essential genes. In addition, VFB detected new driver genes, confirmed in databases such as IntOGen and NCG, and allowed analysis of third-order structures (Betti-3) that was not feasible with VR. Thus, VFB represents a scalable alternative to VR, preserving biological interpretability and complementing classical network metrics. |
| title | Scalable vertex guided filtrations identify structurally relevant genes in cancer networks |
| topic | Molecular Networks |
| url | https://arxiv.org/abs/2605.11450 |