发现B型肝炎相关肝硬化免疫微环境和分子亚型,并通过机器学习和人工神经网络开发稳定的诊断差异模型

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Shengke Zhang¹, Chenglu Jiang¹, Lai Jiang¹

¹Department of Clinical Medicine, School of Clinical Medicine, Affiliated Hospital of Southwest Medical University, Luzhou, China.

Frontiers in molecular biosciences

|October 9, 2023

概括

这项研究确定了六个关键基因,用于使用生物信息学诊断与乙型肝炎相关的肝硬化 (HBV-LC). 这些发现使得HBV-LC患者的早期检测和潜在的治疗策略成为可能.

科学领域:

肝病学肝病学是一种肝病学.
生物信息学是一种生物信息学.
基因组学就是基因组学.

背景情况:

与乙型肝炎相关的肝硬化 (HBV-LC) 显著影响患者的生活质量,并具有挑战性治疗.
目前用于HBV-LC的治疗方法通常是无效的,肝移植受捐赠者的可用性和成本限制.
早期诊断和亚型分类对于管理HBV-LC和预防肝细胞癌 (HCC) 至关重要.

研究的目的:

通过生物信息学分析识别HBV-LC的潜在诊断生物标志物.
使用共识聚类将HBV-LC分为不同的亚型.
开发用于早期诊断和HBV-LC风险分层的预测模型.

主要方法:

使用GEO数据库数据集 (GSE114783,GSE84044) 进行特征基因查.
使用差异分析,WGCNA,LASSO,SVM-RFE和RF用于基因识别.
构建的人工神经网络 (ANN) 和名ogram模型用于预测和验证.

主要成果:

基于六个已识别的特征基因开发了一种ANN模型和名图.
通过共识聚类将HBV-LC分为两种亚型 (C1和C2).
免疫透分析表明C2亚型可能与较轻的临床症状相关.

结论:

该ANN模型和名图显示了HBV-LC诊断的高预测能力.
这些模型为早期检测和治疗HBV-LC提供了一种新的方法.
亚型HBV-LC患者可以指导未来的临床治疗策略.

关键词:

现在,CHB已经成为了CHB.在HBV-LC中,人工神经网络的人工神经网络大数据就是大数据.达成共识的聚类聚类.诊断生物标志物生物标志物免疫透免疫透是什么?机器学习算法的算法

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