Lambert Sophia, Voznica Jakub, Morlon Hélène
Institut de Biologie de l'École Normale Supérieure, École Normale Supérieure, CNRS, INSERM, Université Paris Sciences et Lettres, 46 Rue d'Ulm, 75005 Paris, France.
Institute of Ecology and Evolution, Department of Biology, 5289 University of Oregon, Eugene, OR 97403, USA.
Syst Biol. 2023 Dec 30;72(6):1262-1279. doi: 10.1093/sysbio/syad044.
Birth-death (BD) models are widely used in combination with species phylogenies to study past diversification dynamics. Current inference approaches typically rely on likelihood-based methods. These methods are not generalizable, as a new likelihood formula must be established each time a new model is proposed; for some models, such a formula is not even tractable. Deep learning can bring solutions in such situations, as deep neural networks can be trained to learn the relation between simulations and parameter values as a regression problem. In this paper, we adapt a recently developed deep learning method from pathogen phylodynamics to the case of diversification inference, and we extend its applicability to the case of the inference of state-dependent diversification models from phylogenies associated with trait data. We demonstrate the accuracy and time efficiency of the approach for the time-constant homogeneous BD model and the Binary-State Speciation and Extinction model. Finally, we illustrate the use of the proposed inference machinery by reanalyzing a phylogeny of primates and their associated ecological role as seed dispersers. Deep learning inference provides at least the same accuracy as likelihood-based inference while being faster by several orders of magnitude, offering a promising new inference approach for the deployment of future models in the field.
生灭（BD）模型被广泛地与物种系统发育相结合，用于研究过去的多样化动态。当前的推断方法通常依赖基于似然性的方法。这些方法无法通用，因为每次提出新模型时都必须建立新的似然公式；对于某些模型，这样的公式甚至难以处理。深度学习可以在这种情况下带来解决方案，因为深度神经网络可以作为一个回归问题进行训练，以学习模拟和参数值之间的关系。在本文中，我们将最近从病原体系统动力学中开发的深度学习方法应用于多样化推断的情况，并将其适用性扩展到从与性状数据相关的系统发育中推断状态依赖多样化模型的情况。我们展示了该方法在时间恒定的均匀BD模型和二元状态物种形成与灭绝模型中的准确性和时间效率。最后，我们通过重新分析灵长类动物的系统发育及其作为种子传播者的相关生态作用，说明了所提出的推断机制的使用。深度学习推断至少提供了与基于似然性的推断相同的准确性，同时速度快几个数量级，为该领域未来模型的部署提供了一种有前景的新推断方法。
