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根据RNA-seq和SomaScan数据对源不明的炎症患者进行分类

Orestis D Papagiannopoulos, Konstantina Kourou, Costas Papaloukas

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference

|December 12, 2023

概括

与蛋白质组数据相比,RNA测序显示出在未知起源炎症 (IUO) 中识别生物标志物的前景. 多模式集成并没有显著提高分类准确度,而不是单个RNA测序分析.

科学领域:

生物医学研究的研究.
基因组学和蛋白质组学
计算生物学计算生物学

背景情况:

源不明的炎症 (IUO) 提出了诊断挑战,经常与系统性自身炎症性疾病 (SAID) 重叠.
将IUO与SAID区分开来需要强大的生物标志物识别.
当前的诊断方法可能会从先进的数据整合技术中受益.

研究的目的:

评估RNA测序和SomaScan蛋白质组数据的有效性,用于对源不明的炎症患者进行分类.
评估结合基因组和蛋白质组数据的多式联运数据集成方法的实用性.
确定差异表达的特征,以区分IUO与SAID.

主要方法:

从RNA测序 (基因组) 和SomaScan (蛋白质组) 平台获取数据.
应用一个多式联运数据集成战略,将两个平台结合起来.
使用差异表达特征和错误发现率分析进行分类.
使用后勤回归作为分类任务的学习估计器.

主要成果:

基因组分析 (RNA测序) 的表现优于蛋白质组分析,产生大约19%更高的分类指标.
与单个平台分析相比,多式联运数据集成方法并没有显著提高分类性能.
使用物流回归的最佳个体分析实现了与综合方法相比的分类准确度,灵敏度和特异性.

结论:

RNA测序数据在识别未知来源炎症的潜在生物标志物方面具有显著的优势,即使在与其他系统性自身炎症性疾病区分时也是如此.
这些发现强调了基因组分析在发现IUO特定标记物的潜力.
在这项初步分析中,多模式整合并没有提供优越的分类结果,而仅仅是强大的基因组分析.

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