一种基于蛋白质-蛋白质相互作用网络的修改多重标准决策方法,用于诊断潜在结核病
Somayeh Ayalvari1, Marjan Kaedi2, Mohammadreza Sehhati3
1Faculty of Computer Engineering, University of Isfahan, Isfahan, Iran.
BMC medical informatics and decision making
|October 31, 2024
概括
这项研究引入了一种通过融合基因表达数据来诊断结核病 (TB) 的新方法. 该方法使用机器学习和网络分析的组合,准确区分潜伏结核病感染 (LTBI) 和活跃结核病 (ATB).
科学领域:
- 生物信息学是一种生物信息学.
- 基因组学就是基因组学.
- 计算生物学 计算生物学
背景情况:
- DNA微阵列为转录分析和识别基因表达特征提供了有价值的数据,以防止潜伏性结核病感染 (LTBI) 进展为活跃疾病 (ATB).
- 使用预测模型将LTBI与ATB区分开来是具有挑战性的,因为数据噪声和不稳定的分析方法.
研究的目的:
- 开发一个准确的预测模型来区分LTBI和ATB.
- 为了确定结核病诊断的一组稳定的歧视性基因.
主要方法:
- 提出了一个使用决策层面数据融合的预测模型,将过器和包装器特征选择与多重标准决策 (MCDM) 方法相结合.
- 从六个微阵列数据集中选择了10个歧视性基因.
- 综合蛋白质-蛋白质相互作用 (PPI) 网络与决策试验和评估实验室 (DEMATEL) 方法,以改善特征排名.
主要成果:
- 数据融合方法,结合随机森林 (RF) 和k-最近邻居 (KNN) 分类器,实现了高诊断性能:0.97灵敏度,0.90特异性和0.95准确性.
- 通过累积聚类识别了参与潜伏和活跃结核病诊断的关键基因.
结论:
- 结合MCDM方法和PPI网络,可显著提高不同结核病状态的诊断准确度.
- 拟议的方法提供了一个强大的方法来区分LTBI和ATB.
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