评估抽样偏差对合成和生物网络中节点中心性的影响
Ali Salehzadeh-Yazdi1, Marc-Thorsten Hütt2
1School of Science, Constructor University, Bremen, Germany. asalehzadehyazdi@constructor.university.
NPJ systems biology and applications
|May 15, 2025
概括
抽样偏差显著影响网络分析的中心性指标. 一些方法和网络类型,如蛋白质相互作用,对不完整的数据显示出更大的稳定性,从而提供更可靠的网络分析.
科学领域:
- 网络科学 网络科学
- 计算生物学是一种计算生物学.
- 数据科学是数据科学.
背景情况:
- 中心性测量对于理解复杂网络中节点的重要性至关重要.
- 网络数据经常存在观察错误和不完整性,影响测量的准确性.
研究的目的:
- 调查抽样偏差对网络中心性措施的系统影响.
- 在不同的下方采样场景下评估各种中心性措施的稳定性.
主要方法:
- 在合成和生物网络上模拟了六种类型的偏向下方采样.
- 使用初始密集网络作为基本事实,评估中心性值的变化.
- 在不同网络类型 (合成,蛋白相互作用,代谢物,基因调节,反应) 和中心性措施 (本地与全球) 的强度比较.
主要成果:
- 某些采样方法在合成网络中显示出更高的稳定性,特别是无网络.
- 在生物网络中,蛋白质相互作用网络表现出最强大的稳定性,其次是代谢物,基因调节和反应网络.
- 地方中心性测量通常比全球性测量更强大,显示出异质可靠性.
结论:
- 抽样偏差对网络分析中中心性指标的准确性构成重大限制.
- 了解网络和具体措施的稳定性对于解释不完整的网络数据的结果至关重要.
- 结果指导开发更强大的网络分析方法,适应数据缺陷.
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