使用稀疏的正规相关性分析和合作学习的多式数据融合:一项COVID-19队列研究
Ahmet Gorkem Er1,2,3, Daisy Yi Ding4, Berrin Er5
1Stanford Center for Biomedical Informatics Research (BMIR), Department of Medicine, Stanford University, Stanford, CA, 94305, USA. ahmetgorkemer@gmail.com.
NPJ digital medicine
|May 7, 2024
概括
稀疏的线性方法和合作学习有效地分析多模式COVID-19数据,将生物标志物与成像特征关联起来,并预测患者的结果,如ICU入院. 病毒基因组分析也有助于变种分类.
科学领域:
- 计算生物学是一种计算生物学.
- 医疗信息学医学信息学
- 数据科学是数据科学.
背景情况:
- 高维度生物医学数据为疾病表型和结果提供了洞察力.
- 分析多模式数据 (基因组学,成像,临床,实验室) 提出了重大挑战.
- COVID-19患者数据需要先进的分析技术来全面理解.
研究的目的:
- 用无监督和监督的稀疏线性方法分析来自COVID-19患者队列的多式联络数据.
- 为了确定不同数据模式之间的关系,并预测临床结果.
- 探索病毒基因组编码,用于变种分类和遗传学分析.
主要方法:
- 149名成年COVID-19患者的前性队列研究.
- 对于跨模式数据关系的Sparse法定相关性分析 (CCA).
- 合作学习用于预测重症监护室 (ICU) 的入院.
- Word2Vec用于病毒基因组编码的自然语言处理.
主要成果:
- 血清生物标志物与放射性特征相关联 (cor=0.596,p<0.001).
- 无监督分析显示出不同的临床表型.
- Word2Vec编码分离了SARS-CoV-2变种,并保留了家族遗传关系.
- 一个四倍模型实现了0.87的曲线下面面积 (AUC) 和0.77的结果预测准确度.
结论:
- 稀疏的CCA和合作学习对于高维,多式联络数据分析具有强大作用.
- 这些方法有助于在无监督和监督任务中研究多变量关联.
- 这种方法为了解像COVID-19这样的复杂疾病提供了一个框架.
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