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Updated: Jun 25, 2025

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开发基于长非编码RNA的机器学习模型,以预测COVID-19在医院死亡率

Yvan Devaux¹, Lu Zhang², Andrew I Lumley³

¹Cardiovascular Research Unit, Department of Precision Health, Luxembourg Institute of Health, Strassen, Luxembourg. yvan.devaux@lih.lu.

Nature communications

|May 20, 2024

概括

一个新的机器学习模型使用年龄和长非编码RNA LEF1-AS1.1. 预测COVID-19死亡风险. 这种工具可以改善患者管理,并为SARS-CoV-2感染提供个性化的医疗保健.

科学领域:

生物医学信息学生物医学信息学
基因组学就是基因组学.
计算生物学计算生物学

背景情况:

对COVID-19结果的预测工具对于个性化医疗保健和减少疾病负担至关重要.
机器学习模型有可能准确预测SARS-CoV-2感染患者的严重结果.

研究的目的:

开发和验证用于预测COVID-19患者住院死亡风险的机器学习模型.
确定COVID-19死亡率的关键预测特征,包括遗传标记.

主要方法:

分析了来自欧洲和加拿大队伍 (1286名COVID-19患者) 的血液样本和临床数据 (2020-2023年).
使用向测序对2906个长非编码RNA (lncRNA) 进行分析.
使用年龄和lncRNA LEF1-AS1作为预测特征的前神经网络分类器的开发.

主要成果:

该模型利用年龄和LEF1-AS1,在发现队列中实现了0.83的AUC和0.78的平衡精度.
在加拿大队列中进行的独立验证证实了一致的预测性能.
较高的LEF1-AS1水平与死亡风险降低有关 (HR 0.54).

结论:

结合LEF1-AS1的新型预测模型显示了提高COVID-19患者管理的巨大潜力.
LEF1-AS1是评估SARS-CoV-2感染死亡风险的有希望的生物标志物.
该模型的组件可适应临床医院环境中的测量.

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相关概念视频

lncRNA - Long Non-coding RNAs

lncRNA - Long Non-coding RNAs

In humans, more than 80% of the genome gets transcribed. However, only around 2% of the genome codes for proteins. The remaining part produces non-coding RNAs which includes ribosomal RNAs, transfer RNAs, telomerase RNAs, and regulatory RNAs, among other types. A large number of regulatory non-coding RNAs have been classified into two groups depending upon their length – small non-coding RNAs, such as microRNA, which are less than 200 nucleotides in length, and long non-coding RNA...