Ma Eric J, Kummer Arkadij
Independent Researcher, Cambridge, MA 02139, USA.
Department of Biosystems Science and Engineering, ETH Zurich, Mattenstrasse 26, 4058 Basel, Switzerland.
Entropy (Basel). 2021 Jun 8;23(6):727. doi: 10.3390/e23060727.
We present a case study applying hierarchical Bayesian estimation on high-throughput protein melting-point data measured across the tree of life. We show that the model is able to impute reasonable melting temperatures even in the face of unreasonably noisy data. Additionally, we demonstrate how to use the variance in melting-temperature posterior-distribution estimates to enable principled decision-making in common high-throughput measurement tasks, and contrast the decision-making workflow against simple maximum-likelihood curve-fitting. We conclude with a discussion of the relative merits of each workflow.
我们展示了一个案例研究，该研究将分层贝叶斯估计应用于在生命之树上测量的高通量蛋白质熔点数据。我们表明，即使面对不合理的噪声数据，该模型也能够估算出合理的熔化温度。此外，我们演示了如何利用熔化温度后验分布估计中的方差，在常见的高通量测量任务中进行有原则的决策，并将决策工作流程与简单的最大似然曲线拟合进行对比。我们最后讨论了每个工作流程的相对优点。
