Quantum Network-Based Prediction of Cancer Driver Genes

• URL: http://arxiv.org/abs/2510.12628v2
• Date: Tue, 04 Nov 2025 16:54:30 GMT
• Title: Quantum Network-Based Prediction of Cancer Driver Genes
• Authors: Patricia Marques, Andreas Wichert, Duarte Magano, Bruno Coutinho,
• Abstract summary: We introduce a supervised quantum framework that combines mutation scores with network topology via a novel state preparation scheme.<n>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.<n> Simulations on an empirical PPI network demonstrate competitive performance, with a 12.6% recall gain over a classical baseline.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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