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通过协作进行验证:鼓励团队努力确保生化途径计算模型的内部和外部有效性。

Validation Through Collaboration: Encouraging Team Efforts to Ensure Internal and External Validity of Computational Models of Biochemical Pathways.

机构信息

Centre for Discovery Brain Sciences, University of Edinburgh, Edinburgh, UK.

School of Biological Sciences, University of Edinburgh, Edinburgh, UK.

出版信息

Neuroinformatics. 2022 Jan;20(1):277-284. doi: 10.1007/s12021-022-09584-5. Epub 2022 May 11.

Abstract

Computational modelling of biochemical reaction pathways is an increasingly important part of neuroscience research. In order to be useful, computational models need to be valid in two senses: First, they need to be consistent with experimental data and able to make testable predictions (external validity). Second, they need to be internally consistent and independently reproducible (internal validity). Here, we discuss both types of validity and provide a brief overview of tools and technologies used to ensure they are met. We also suggest the introduction of new collaborative technologies to ensure model validity: an incentivised experimental database for external validity and reproducibility audits for internal validity. Both rely on FAIR principles and on collaborative science practices.

摘要

生化反应途径的计算建模是神经科学研究中越来越重要的一部分。为了有用,计算模型需要在两个方面具有有效性:首先,它们需要与实验数据一致,并能够做出可测试的预测(外部有效性)。其次,它们需要内部一致且可以独立重现(内部有效性)。在这里,我们讨论了这两种有效性,并简要介绍了用于确保满足这些有效性的工具和技术。我们还建议引入新的协作技术来确保模型的有效性:用于外部有效性的激励性实验数据库和用于内部有效性的可重复性审核。这两者都依赖于 FAIR 原则和协作科学实践。

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