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首页> 外文期刊>IFAC PapersOnLine >New Concepts for Evaluating the Performance of Computational Methods * * The author acknowledge financial support by the by the German Ministry of Education and Research (BMBF) via e:Bio Grant No. 031L0080.
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New Concepts for Evaluating the Performance of Computational Methods * * The author acknowledge financial support by the by the German Ministry of Education and Research (BMBF) via e:Bio Grant No. 031L0080.

机译：评估计算方法性能的新概念 * * 作者感谢德国教育和研究部的财政支持（BMBF）通过e：Bio授予编号031L0080。

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Abstract: Research in Systems Biology is currently entering a new era. After a decade characterized by adopting existing experimental protocols and theoretical approaches to the requirements of Systems Biology, there is now a variety of tools and approaches available. However, many statistical and modeling concepts are not well-tested in application settings and their applicability is often seriously delimited. Therefore, a major challenge for the transfer of theoretical approaches to applications is the assessment and optimization of the methods’ performance for supporting experimental research. In this paper, new concepts for assessing methods which were developed for analyzing experimental data in the context of systems biology will be introduced. Some ideas are illustrated by evaluating the impact of the logarithmic transformation for parameter estimation. A strong benefit of the log-transformation was observed for five different ODE models. The suggested framework enables less biased and more reliable and valid assessment and comparison of competing approaches than currently performed in the literature. The presented concepts could serve as basis for developing decision guidelines for optimal selection of analysis methods and thereby enhancing the transfer of systems biological procedures and reverse engineering methods to industrial applications like drug development.
机译：摘要：系统生物学研究正在进入一个新时代。在采用现有的实验方案和理论方法满足系统生物学要求的十年后，现在有了各种工具和方法。但是，许多统计和建模概念在应用程序设置中没有经过充分测试，其适用性通常受到严重限制。因此，将理论方法应用于应用的主要挑战是评估和优化方法性能以支持实验研究。在本文中，将介绍用于评估系统生物学背景下的实验数据的评估方法的新概念。通过评估对数变换对参数估计的影响来说明一些想法。在五个不同的ODE模型中，观察到了对数转换的强大优势。所提出的框架与文献中目前进行的方法相比，可以使对竞争方法的偏见更少，更可靠，更有效，更有效地进行评估和比较。提出的概念可以作为开发分析方法最佳选择的决策指南的基础，从而增强系统生物学程序和逆向工程方法向工业应用（如药物开发）的转移。

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