标题： 基于DNA微阵列表达谱的量子增强脑肿瘤分类 显示英文标题

标题： Quantum-Enhanced Classification of Brain Tumors Using DNA Microarray Gene Expression Profiles

摘要： DNA微阵列技术使得同时测量数千个基因的表达水平成为可能，从而有助于理解脑肿瘤等复杂疾病的分子机制，并识别诊断的遗传特征。为了从该技术获得的高维和复杂的基因特征中得出有意义的生物学见解，并详细分析基因特性，广泛采用基于经典AI的方法，如机器学习和深度学习。然而，这些方法在管理高维向量空间和建模基因间的复杂关系时面临各种限制。特别是，超参数调整、计算成本和高处理能力需求等问题可能会阻碍它们的效率。为了解决这些限制，量子计算和量子AI方法引起了越来越多的关注。利用量子叠加和纠缠等量子特性，量子方法能够更有效地并行处理高维数据，并为那些对于经典方法来说计算要求高的问题提供更快、更有效的解决方案。在本研究中，提出了一种基于变分量子分类器方法的新模型，称为“深度VQC”。该模型使用包含54,676个基因特征的微阵列数据成功地对四种不同类型的脑肿瘤——室管膜瘤、胶质母细胞瘤、髓母细胞瘤和毛细胞星形细胞瘤——以及健康样本进行了高精度分类。此外，与经典机器学习算法相比，我们的模型展示了优越或相当的分类性能。这些结果突显了量子AI方法作为有效且有前景的方法，用于基于基因表达特征分析和分类脑肿瘤等复杂结构的潜力。 显示英文摘要

摘要： DNA microarray technology enables the simultaneous measurement of expression levels of thousands of genes, thereby facilitating the understanding of the molecular mechanisms underlying complex diseases such as brain tumors and the identification of diagnostic genetic signatures. To derive meaningful biological insights from the high-dimensional and complex gene features obtained through this technology and to analyze gene properties in detail, classical AI-based approaches such as machine learning and deep learning are widely employed. However, these methods face various limitations in managing high-dimensional vector spaces and modeling the intricate relationships among genes. In particular, challenges such as hyperparameter tuning, computational costs, and high processing power requirements can hinder their efficiency. To overcome these limitations, quantum computing and quantum AI approaches are gaining increasing attention. Leveraging quantum properties such as superposition and entanglement, quantum methods enable more efficient parallel processing of high-dimensional data and offer faster and more effective solutions to problems that are computationally demanding for classical methods. In this study, a novel model called "Deep VQC" is proposed, based on the Variational Quantum Classifier approach. Developed using microarray data containing 54,676 gene features, the model successfully classified four different types of brain tumors-ependymoma, glioblastoma, medulloblastoma, and pilocytic astrocytoma-alongside healthy samples with high accuracy. Furthermore, compared to classical ML algorithms, our model demonstrated either superior or comparable classification performance. These results highlight the potential of quantum AI methods as an effective and promising approach for the analysis and classification of complex structures such as brain tumors based on gene expression features.