人工智能
随机森林
回归
机器学习
计算机科学
功能(生物学)
公制(单位)
主成分分析
回归分析
线性回归
生物学数据
统计
生物信息学
生物
数学
工程类
运营管理
进化生物学
作者
James J. Tronolone,Tanmay Mathur,Christopher P. Chaftari,Abhishek Jain
标识
DOI:10.1007/s10439-023-03177-2
摘要
Vascularized microphysiological systems and organoids are contemporary preclinical experimental platforms representing human tissue or organ function in health and disease. While vascularization is emerging as a necessary physiological organ-level feature required in most such systems, there is no standard tool or morphological metric to measure the performance or biological function of vascularized networks within these models. Further, the commonly reported morphological metrics may not correlate to the network's biological function—oxygen transport. Here, a large library of vascular network images was analyzed by the measure of each sample's morphology and oxygen transport potential. The oxygen transport quantification is computationally expensive and user-dependent, so machine learning techniques were examined to generate regression models relating morphology to function. Principal component and factor analyses were applied to reduce dimensionality of the multivariate dataset, followed by multiple linear regression and tree-based regression analyses. These examinations reveal that while several morphological data relate poorly to the biological function, some machine learning models possess a relatively improved, but still moderate predictive potential. Overall, random forest regression model correlates to the biological function of vascular networks with relatively higher accuracy than other regression models.
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