Machine learning-based data analytic approaches for evaluating post-natal mouse respiratory physiological evolution.
Machine learning-based data analytic approaches for evaluating post-natal mouse respiratory physiological evolution.
复制标题
DOI：
10.1016/j.resp.2020.103558
复制
发表时间：
2021-01
影响因子：
2.3
通讯作者：
Otero JJ
中科院分区：
文献类型：
--
作者：
Wang W;Alzate-Correa D;Alves MJ;Jones M;Garcia AJ 3rd;Zhao J;Czeisler CM;Otero JJ
呼吸参数在出生后发育过程中发生变化，但其变化的性质尚未得到很好的描述。市售体积描记仪器的出现提供了改进的测量可重复性和跨实验室测量的小鼠呼吸的标准化。因此，这些技术允许在出生后发育时期探索更精确的呼吸模式变化。目前分析呼吸行为的方法利用体积描记法来获取在鼠成熟的特定时间点的频率、体积和流量的固定值。这些指标历来作为时间的函数进行独立分析，没有进一步分析检查这些变量彼此之间以及在出生后成熟或血气稳态期间的相互作用。我们认为，机器学习工作流程可以为呼吸的出生后发育提供更深入的生理理解。在本文中，我们描述了一个基于R-统计编程语言的机器学习工作流程，以检查频率（f）和潮气量（TV）的变化和关系如何随吸气和呼气参数而变化。我们的分析工作流程可以成功地预测年龄，并发现呼吸指标之间的变化和关系随着年龄和高碳酸血症呼吸而动态变化。因此，我们的工作证明了使用非侵入性呼吸指标提供可靠的类别标签预测的高维分析的实用性。这些方法可能有助于跨发育和疾病的大规模表型分析。
Respiratory parameters change during post-natal development, but the nature of their changes have not been well-described. The advent of commercially available plethysmographic instruments provided improved repeatability of measurements and standardization of measured breathing in mice across laboratories. These technologies thus allowed for exploration of more precise respiratory pattern changes during the post-natal developmental epoch. Current methods to analyze respiratory behavior utilize plethysmography to acquire standing values of frequency, volume and flow at specific time points in murine maturation. These metrics have historically been independently analyzed as a function of time with no further analysis examining the interplay these variables have with each other and in the context of postnatal maturation or during blood gas homeostasis. We posit that machine learning workflows can provide deeper physiological understanding into the postnatal development of respiration. In this manuscript, we delineate a machine learning workflow based on the R-statistical programming language to examine how variation and relationships of frequency (f) and tidal volume (TV) change with respect to inspiratory and expiratory parameters. Our analytical workflows could successfully predict age and found that the variation and relationships between respiratory metrics are dynamically shifting with age and during hypercapnic breathing. Thus, our work demonstrates the utility of high dimensional analyses to provide reliable class label predictions using non-invasive respiratory metrics. These approaches may be useful in large-scale phenotyping across development and in disease.
登录
查看更多内容
区域postrema经历小鼠和人类的产后变化。
DOI：
10.1002/cne.23903
发表时间：
2016-04-15
期刊：
The Journal of comparative neurology
影响因子：
--
作者：
Gokozan HN;Baig F;Corcoran S;Catacutan FP;Gygli PE;Takakura AC;Moreira TS;Czeisler C;Otero JJ
通讯作者：
Otero JJ
开发一种基于斐济的新方法，用于研究新生儿小鼠的神经元电路。
影响因子：
3
作者：
Mei-Ling Liu J;Fair SR;Kaya B;Zuniga JN;Mostafa HR;Alves MJ;Stephens JA;Jones M;Aslan MT;Czeisler C;Otero JJ
通讯作者：
Otero JJ
影响因子：
2.5
作者：
Ide, S;Itoh, M;Goto, Y
通讯作者：
Goto, Y
DOI：
10.1007/978-1-62703-658-0_11
发表时间：
2014
期刊：
Methods in molecular biology (Clifton, N.J.)
影响因子：
--
作者：
David CC;Jacobs DJ
通讯作者：
Jacobs DJ
影响因子：
--
作者：
CHAPIN, JL
通讯作者：
CHAPIN, JL