Mendes Fábio K, Bouckaert Remco, Carvalho Luiz M, Drummond Alexei J
Department of Biological Sciences, Louisiana State University, Baton Rouge, LA 70803, USA.
School of Computer Science, The University of Auckland, Auckland 1010, New Zealand.
Syst Biol. 2025 Feb 10;74(1):158-175. doi: 10.1093/sysbio/syae064.
Biology has become a highly mathematical discipline in which probabilistic models play a central role. As a result, research in the biological sciences is now dependent on computational tools capable of carrying out complex analyses. These tools must be validated before they can be used, but what is understood as validation varies widely among methodological contributions. This may be a consequence of the still embryonic stage of the literature on statistical software validation for computational biology. Our manuscript aims to advance this literature. Here, we describe, illustrate, and introduce new good practices for assessing the correctness of a model implementation with an emphasis on Bayesian methods. We also introduce a suite of functionalities for automating validation protocols. It is our hope that the guidelines presented here help sharpen the focus of discussions on (as well as elevate) expected standards of statistical software for biology.
生物学已成为一门高度数学化的学科，概率模型在其中发挥着核心作用。因此，生物科学研究现在依赖于能够进行复杂分析的计算工具。这些工具在使用前必须经过验证，但对于验证的理解在不同的方法学贡献中差异很大。这可能是由于计算生物学统计软件验证文献仍处于萌芽阶段。我们的手稿旨在推动这方面的文献发展。在这里，我们描述、说明并介绍了评估模型实现正确性的新的良好实践，重点是贝叶斯方法。我们还引入了一套用于自动化验证协议的功能。我们希望这里提出的指导方针有助于明确关于生物学统计软件预期标准的讨论重点（并提高这些标准）。
