Structural information of phylogenetic tree topologies plays an important role in phylogenetic inference. However, finding appropriate topological structures for specific phylogenetic inference tasks often requires significant design effort and domain expertise. In this paper, we propose a novel structural representation method for phylogenetic inference based on learnable topological features. By combining the raw node features that minimize the Dirichlet energy with modern graph representation learning techniques, our learnable topological features can provide efficient structural information of phylogenetic trees that automatically adapts to different downstream tasks without requiring domain expertise. We demonstrate the effectiveness and efficiency of our method on a simulated data tree probability estimation task and a benchmark of challenging real data variational Bayesian phylogenetic inference problems.

系统发育树拓扑的结构信息起着重要的作用 在系统发育推论中的作用。然而，找到合适的拓扑结构 用于特定系统发育推断任务的结构通常需要显著的 设计工作和领域专业知识。在这篇文章中，我们提出了一部小说 基于可学习的系统发育推理的结构表示方法 拓扑特征。通过组合原始节点功能来最小化 Dirichlet能量与现代图形表示学习技术，我们的 可学习的拓扑特征可以提供有效的结构信息 自动适应不同下游任务的系统发育树 而不需要领域专业知识。我们展示了它的有效性和 我们的方法在模拟数据树概率估计任务中的有效性 和挑战真实数据变分贝叶斯系统学的基准 推理问题。