一个新的计算工作流来指导个性化药物治疗
Simone Pernice1, Alessandro Maglione2, Dora Tortarolo3
1Department of Computer Science, University of Turin, Corso Svizzera 185, Turin, 10149, Italy; CINI Infolife laboratory, Turin, Italy.
Journal of biomedical informatics
|November 20, 2023
概括
一个计算工具GreatNectorworkflow分析患者数据,以预测疾病动态,并为个性化医学分层个体. 它确定了多发性硬化症患者的T细胞变化,区分了响应者和不响应者.
科学领域:
- 计算生物学是一种计算生物学.
- 系统生物学 系统生物学
- 精准医学是一门精准的医学.
背景情况:
- 个性化医疗依赖于计算模型来分析复杂,异质的患者数据.
- 患者分层对于有效的精准医学至关重要,因为个体的变化和环境因素.
研究的目的:
- 展示GreatNector工作流作为一种用于分析纵向患者数据和模拟患者特定疾病动态的工具.
- 为了实现针对性干预的患者分层.
主要方法:
- GreatNector工作流集成了用于纵向数据分析的CONNECTOR和基于Petri网的定量建模框架GreatMod,用于无监督主题分层.
- 该框架将数据驱动分析与疾病动态模拟的定量建模相结合.
主要成果:
- 来自多发性硬化症患者的纵向数据显示,在alemtuzumab治疗后,T细胞动态有所不同,将响应者与不响应者区分开来.
- 没有反应的人在36个月左右显示Th17度增加.
- GreatNector的工作流将患者分为三个不同的模型元患者.
结论:
- GreatNector的工作流有效地根据患者的疾病动态对患者进行分层.
- 确定的患者子组为开发准确医学中针对患者量身定制的干预提供了洞察力.
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