Department of Computer Science, Indiana University, Bloomington, IN 47405, USA.
Department of Computer Science, Indiana University, Bloomington, IN 47405, USA; Department of Biology, Indiana University, Bloomington, IN 47405, USA.
Mol Phylogenet Evol. 2024 Jul;196:108066. doi: 10.1016/j.ympev.2024.108066. Epub 2024 Mar 31.
Machine learning has increasingly been applied to a wide range of questions in phylogenetic inference. Supervised machine learning approaches that rely on simulated training data have been used to infer tree topologies and branch lengths, to select substitution models, and to perform downstream inferences of introgression and diversification. Here, we review how researchers have used several promising machine learning approaches to make phylogenetic inferences. Despite the promise of these methods, several barriers prevent supervised machine learning from reaching its full potential in phylogenetics. We discuss these barriers and potential paths forward. In the future, we expect that the application of careful network designs and data encodings will allow supervised machine learning to accommodate the complex processes that continue to confound traditional phylogenetic methods.
机器学习在系统发育推断的各个方面得到了越来越广泛的应用。依赖于模拟训练数据的有监督机器学习方法已经被用于推断树拓扑和分支长度、选择替代模型以及进行基因渗入和多样化的下游推断。在这里，我们回顾了研究人员如何使用几种有前途的机器学习方法来进行系统发育推断。尽管这些方法很有前景，但有几个障碍阻碍了监督机器学习在系统发生学中充分发挥其潜力。我们讨论了这些障碍和潜在的前进道路。未来，我们预计仔细的网络设计和数据编码的应用将允许监督机器学习适应继续困扰传统系统发育方法的复杂过程。
