用量子支持向量机从新冠多组学数据中找关键生物标志物
Can a Quantum Support Vector Machine algorithm be utilized to identify Key Biomarkers from Multi-Omics data of COVID19 patients?
- 用量子核函数实现多组学数据分类，对比经典支持向量机
- 量子模型性能与经典模型相当甚至更优，且符合标志物重要性排序
- 适合对量子机器学习在生物医学应用感兴趣的科研人员
从高维多组学数据中识别新冠关键生物标志物对诊断和发病机制研究至关重要。本研究评估了量子支持向量机（QSVM）在新冠生物标志物分类中的适用性。基于两个独立数据集的蛋白质组和代谢组数据，使用岭回归对生物标志物进行重要性排序并分组。将排名靠前和靠后的标志物集分别作为预测和负控输入，用于训练和评估经典支持向量机（CSVM）与QSVM模型。实验采用多种量子核函数，包括幅值编码、角度编码、ZZ特征映射和投影量子核。在不同实验条件下，QSVM分类性能始终与或优于CSVM，且结果与岭回归的重要性排序一致。尽管实验为数值模拟，但结果表明QSVM在生物医学多组学分析中具有潜力。
原文摘要 · Abstract (English)
Identifying key biomarkers for COVID-19 from high-dimensional multi-omics data is critical for advancing both diagnostic and pathogenesis research. In this study, we evaluated the applicability of the Quantum Support Vector Machine (QSVM) algorithm for biomarker-based classification of COVID-19. Proteomic and metabolomic biomarkers from two independent datasets were ranked by importance using ridge regression and grouped accordingly. The top- and bottom-ranked biomarker sets were then used to train and evaluate both classical SVM (CSVM) and QSVM models, serving as predictive and negative control inputs, respectively. The QSVM was implemented with multiple quantum kernels, including amplitude encoding, angle encoding, the ZZ feature map, and the projected quantum kernel. Across various experimental settings, QSVM consistently achieved classification performance that was comparable to or exceeded that of CSVM, while reflecting the importance rankings by ridge regression. Although the experiments were conducted in numerical simulation, our findings highlight the potential of QSVM as a promising approach for multi-omics data analysis in biomedical research.
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