GatekeepR:一种R Shiny应用程序,用于识别布尔网络中具有高动态影响的节点
Felix M Weidner1, Nensi Ikonomi1, Silke D Werle1
1Institute of Medical Systems Biology, Ulm University, Albert-Einstein-Allee 11, Ulm 89081, Germany.
Bioinformatics (Oxford, England)
|January 10, 2024
概括
GatekeepR是一个Web应用程序,帮助科学家在大型布尔网络中识别关键干预目标. 它简化了复杂的分析,使其可以在没有R编程知识的情况下使用.
科学领域:
- 系统生物学 系统生物学
- 计算生物学 计算生物学
- 生物信息学是一种生物信息学.
背景情况:
- 布尔网络使用逻辑规则来模拟复杂的生物过程,例如基因调节.
- 由于状态空间的扩张,分析大型网络的干预目标在计算上具有挑战性.
- 现有的方法往往需要编程专业知识,限制了可访问性.
研究的目的:
- 开发一个用户友好的应用程序,用于在布尔网络中识别有影响力的干预目标.
- 为更广泛的科学受众提供先进的网络分析方法.
- 为了促进大规模模型中的潜在目标的选和集中调查.
主要方法:
- 介绍GatekeepR,一个图形,基于Web的R Shiny应用程序.
- 使用逻辑规则和交互图来确定干预目标.
- 为直观分析提供网络信息和可视化.
主要成果:
- GatekeepR允许对布尔网络模型进行高影响干预目标的选.
- 该应用程序不需要本地安装或R编程知识.
- 在胰腺癌网络建模中的一个例子中展示了实用性.
结论:
- 在布尔网络中,GatekeepR使干预目标的识别民主化.
- 该工具增强了科学家分析复杂生物系统的能力.
- 通过预先过有前途的候选目标来促进动态分析.
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