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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2407.19330 |
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| _version_ | 1866909713180917760 |
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| author | Margarit, David H. Paccosi, Gustavo Reale, Marcela V. Romanelli, Lilia M. |
| author_facet | Margarit, David H. Paccosi, Gustavo Reale, Marcela V. Romanelli, Lilia M. |
| contents | We propose a novel computational framework leveraging hypergraph theory to analyse cancer stem cell markers (CSCMs) across multiple organs. Hypergraphs provide a robust representation of CSCM co-expression patterns, capturing their complex multi-organ relationships more comprehensively than traditional graph-based methods. By integrating mutual information analysis and Markov models, we identify key markers driving tumour heterogeneity and metastasis, offering detailed insights into their interdependencies. This approach establishes hypergraphs as a computationally powerful tool to model cancer progression and metastatic dynamics, contributing to the understanding of complex biological systems and supporting the development of targeted therapeutic strategies. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_19330 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Unveiling Cancer Stem Cell Marker Networks: A Hypergraph Approach Margarit, David H. Paccosi, Gustavo Reale, Marcela V. Romanelli, Lilia M. Biological Physics Quantitative Methods 05C65, 62P10, 92C05, 60J20 I.6.0; G.2.2; G.2.3 We propose a novel computational framework leveraging hypergraph theory to analyse cancer stem cell markers (CSCMs) across multiple organs. Hypergraphs provide a robust representation of CSCM co-expression patterns, capturing their complex multi-organ relationships more comprehensively than traditional graph-based methods. By integrating mutual information analysis and Markov models, we identify key markers driving tumour heterogeneity and metastasis, offering detailed insights into their interdependencies. This approach establishes hypergraphs as a computationally powerful tool to model cancer progression and metastatic dynamics, contributing to the understanding of complex biological systems and supporting the development of targeted therapeutic strategies. |
| title | Unveiling Cancer Stem Cell Marker Networks: A Hypergraph Approach |
| topic | Biological Physics Quantitative Methods 05C65, 62P10, 92C05, 60J20 I.6.0; G.2.2; G.2.3 |
| url | https://arxiv.org/abs/2407.19330 |