Branching in vascular networks and in overall organismic form is one of the most common and ancient features of multicellular plants, fungi and animals. By combining machine-learning techniques with new theory that relates vascular form to metabolic function, we enable novel classification of diverse branching networks—mouse lung, human head and torso, angiosperm and gymnosperm plants. We find that ratios of limb radii—which dictate essential biologic functions related to resource transport and supply—are best at distinguishing branching networks. We also show how variation in vascular and branching geometry persists despite observing a convergent relationship across organisms for how metabolic rate depends on body mass.

中文翻译：

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通过结合大量数据、机器学习和建模确定动植物网络的分支原理

血管网络和整体有机体形式的分支是多细胞植物、真菌和动物最常见和最古老的特征之一。通过将机器学习技术与将血管形式与代谢功能联系起来的新理论相结合，我们实现了对不同分支网络的新分类——小鼠肺、人头和躯干、被子植物和裸子植物。我们发现肢体半径的比率——决定了与资源运输和供应相关的基本生物功能——最能区分分支网络。我们还展示了血管和分支几何结构的变化如何持续存在，尽管观察到生物体之间代谢率如何取决于体重的收敛关系。