DataXflow:与最佳参数匹配和最佳控制相结合的数据驱动建模 - - 癌症研究的高效数据分析
Samantha A W Crouch1, Jan Krause1, Thomas Dandekar1
1Department of Bioinformatics, Biocenter, University of Würzburg, Am Hubland 97074, Würzburg, Germany.
概括
数据Xflow建立数据驱动的模型,以有效地识别药物目标. 这一框架通过确定最佳的治疗干预措施来加速癌症研究,例如抑制AURKA和激活肺癌的CDH1.
科学领域:
- 计算生物学 计算生物学
- 系统生物学 系统生物学
- 生物信息学是一种生物信息学.
背景情况:
- 数据驱动模型对于从实证数据中提取信息至关重要.
- 数学模型通过调整参数以适应特定数据来编码相关信息.
- 最优的控制框架通过外部刺激来识别方向盘模型的高效目标.
研究的目的:
- 推出DataXflow,一个集成数据驱动建模和最佳控制的软件框架,用于有效识别目标.
- 为了自动化复杂的建模任务,包括方程和脚本生成的系统与许多代理.
- 降低系统生物学和计算药物发现研究人员的编程障碍.
主要方法:
- DataXflow集成了三个管道:D2D用于模型拟合,对外部刺激的最佳控制框架,以及JimenaE用于图形用户界面.
- 从图表和脚本生成中生成方程,用于像基因调节网络这样的复杂网络.
- 代建模过程,以完善模型拓,并从数据中生成监管网络.
主要成果:
- DataXflow成功地确定了用于肺癌治疗的药物标,旨在减少扩散和增加亡.
- 最佳控制框架显示,抑制AURKA和激活CDH1是最有效的药物标组合.
- 该框架为加速研究的数据生成和分析提供了灵活的相互作用.
结论:
- 数据Xflow能够有效地识别药物点,即使是在复杂的生物网络中.
- 该软件通过提供对治疗干预建模的简化方法来加速癌症研究.
- 这种方法支持敏捷的数据分析,并可能更快地发现新的癌症疗法.
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