用量子机器学习分析多组学数据，区分肺癌亚型。
Multi-Omic and Quantum Machine Learning Integration for Lung Subtypes Classification
- 结合量子机器学习与多组学数据，挖掘肺癌亚型差异特征。
- 识别出可区分肺腺癌与鳞癌的关键生物标志物。
- 适合对量子计算和精准医疗交叉研究感兴趣者阅读。
量子机器学习（QML）是当前热门领域，为解决各类计算难题带来新发现与机遇。在生物医学研究与个性化医疗中，多组学整合能全面理解复杂生物系统，推动基础研究向临床转化。通过整合基因表达、微RNA及DNA甲基化等多维度数据，可为疾病诊断、预后判断和治疗方案制定提供支持。针对肺癌多组学数据高维、样本少、异质性强的特点，本文提出一种基于量子机器学习的多组学数据融合方法，旨在实现肺腺癌（LUAD-II）与肺鳞癌（LUSC-I）亚型的精准分类，并筛选出最具区分能力的特征，具有潜在生物标志物发现价值。
原文摘要 · Abstract (English)
Quantum Machine Learning (QML) is a red-hot field that brings novel discoveries and exciting opportunities to resolve, speed up, or refine the analysis of a wide range of computational problems. In the realm of biomedical research and personalized medicine, the significance of multi-omics integration lies in its ability to provide a thorough and holistic comprehension of complex biological systems. This technology links fundamental research to clinical practice. The insights gained from integrated omics data can be translated into clinical tools for diagnosis, prognosis, and treatment planning. The fusion of quantum computing and machine learning holds promise for unraveling complex patterns within multi-omics datasets, providing unprecedented insights into the molecular landscape of lung cancer. Due to the heterogeneity, complexity, and high dimensionality of multi-omic cancer data, characterized by the vast number of features (such as gene expression, micro-RNA, and DNA methylation) relative to the limited number of lung cancer patient samples, our prime motivation for this paper is the integration of multi-omic data, unique feature selection, and diagnostic classification of lung subtypes: lung squamous cell carcinoma (LUSC-I) and lung adenocarcinoma (LUAD-II) using quantum machine learning. We developed a method for finding the best differentiating features between LUAD and LUSC datasets, which has the potential for biomarker discovery.
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