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Yongliang Chen¹, Jing Xue², Xiaoli Yan²

¹Department of Cardiac Surgery, Affiliated Hospital of Chengde Medical University, 36 Nanyingzi Street, Chengde, Hebei, 067000, China.

BMC cardiovascular disorders

|July 28, 2023

概括

这项研究使用机器学习和高通量测序来确定心力衰竭 (HF) 的16个关键基因. 开发了一种RNA诊断模型,在预测HF方面表现出高准确度,为其发病过程提供了新的见解.

科学领域:

基因组学和生物信息学
心血管研究研究心血管研究
人工智能在医学中的应用

背景情况:

心力衰竭 (HF) 的分子机制尚未完全理解.
高通量测序和人工智能等创新方法对于高频研究至关重要.
调查HF的发病,诊断和治疗需要先进的方法.

研究的目的:

使用生物信息学识别高频特异性基因.
开发和验证基于机器学习的高频诊断模型.
探索免疫细胞透在HF病变发生中的作用.

主要方法:

对心力衰竭和对照样本进行选的GEO数据集 (GSE3586,GSE5406,GSE57345).
鉴定了差异表达基因 (DEGs) 并进行了功能丰富分析 (GO,KEGG).
构建了一个随机森林的人工神经网络,并使用ROC曲线进行验证;分析了免疫细胞透.

主要成果:

在HF中确定了153个重要的DEG,其中81个是下调和72个是上调.
下调的基因富含有机离子运输和PI3K-Akt通路;上调的基因在中性粒细胞激活和信号传递中.
开发了一个具有16个高频基因的模型,AUC达到0.996 (火车) 和0.863 (测试);证实了诊断准确性和稳定性.

结论:

揭示了HF进展中的潜在功能和途径.
使用机器学习设计了一种用于HF组织的新型RNA诊断模型.
使用ROC曲线验证了模型的灵敏度,特异性和稳定性.

关键词:

人工神经网络的人工神经网络诊断诊断诊断诊断诊断心脏衰竭是因为心脏衰竭.随机的森林随机的森林支持向量机器支持向量机器

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