Quantum Network-Based Prediction of Cancer Driver Genes
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arXiv
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| Autores principales: | , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866908627928875008 |
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| author | Marques, Patricia Wichert, Andreas Magano, Duarte Coutinho, Bruno |
| author_facet | Marques, Patricia Wichert, Andreas Magano, Duarte Coutinho, Bruno |
| contents | Identification of cancer driver genes is fundamental for the development of targeted therapeutic interventions. The integration of mutational profiles with protein-protein interaction (PPI) networks offers a promising avenue for their detection [ 1, 2], but scaling to large network datasets is computationally demanding. Quantum computing offers compact representations and potential complexity reductions. Motivated by the classical method of Gumpinger et al. [3], in this work we introduce a supervised quantum framework that combines mutation scores with network topology via a novel state preparation scheme, Quantum Multi-order Moment Embedding (QMME). QMME encodes low-order statistical moments over the mutation scores of a node's immediate and second-order neighbors, and encodes this information into quantum states. These are used as inputs to a kernel-based quantum binary classifier that discriminates known driver genes from others. Simulations on an empirical PPI network demonstrate competitive performance, with a 12.6% recall gain over a classical baseline. The pipeline performs explicit quantum state preparation and requires no classical training, enabling an efficient, nearly end-to-end quantum workflow. A brief complexity analysis suggests the approach could achieve a quantum speedup in network-based cancer gene prediction. This work underscores the potential of supervised quantum graph learning frameworks to advance biological discovery. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_12628 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Quantum Network-Based Prediction of Cancer Driver Genes Marques, Patricia Wichert, Andreas Magano, Duarte Coutinho, Bruno Quantum Physics Identification of cancer driver genes is fundamental for the development of targeted therapeutic interventions. The integration of mutational profiles with protein-protein interaction (PPI) networks offers a promising avenue for their detection [ 1, 2], but scaling to large network datasets is computationally demanding. Quantum computing offers compact representations and potential complexity reductions. Motivated by the classical method of Gumpinger et al. [3], in this work we introduce a supervised quantum framework that combines mutation scores with network topology via a novel state preparation scheme, Quantum Multi-order Moment Embedding (QMME). QMME encodes low-order statistical moments over the mutation scores of a node's immediate and second-order neighbors, and encodes this information into quantum states. These are used as inputs to a kernel-based quantum binary classifier that discriminates known driver genes from others. Simulations on an empirical PPI network demonstrate competitive performance, with a 12.6% recall gain over a classical baseline. The pipeline performs explicit quantum state preparation and requires no classical training, enabling an efficient, nearly end-to-end quantum workflow. A brief complexity analysis suggests the approach could achieve a quantum speedup in network-based cancer gene prediction. This work underscores the potential of supervised quantum graph learning frameworks to advance biological discovery. |
| title | Quantum Network-Based Prediction of Cancer Driver Genes |
| topic | Quantum Physics |
| url | https://arxiv.org/abs/2510.12628 |