Learnable Topological Features for Phylogenetic Inference via Graph Neural Networks
Learnable Topological Features for Phylogenetic Inference via Graph Neural Networks
复制标题
DOI：
10.48550/arxiv.2302.08840
复制
发表时间：
2023-02
期刊：
影响因子：
--
通讯作者：
Cheng Zhang
中科院分区：
文献类型：
--
作者：
Cheng Zhang
系统发育树拓扑结构信息在系统发育推断中起着重要作用。然而，为特定的系统发育推断任务找到合适的拓扑结构通常需要大量的设计工作和领域专业知识。本文提出了一种基于可学习拓扑特征的系统发育推理结构表示方法。通过将Dirichlet能量最小化的原始节点特征与现代图表示学习技术相结合，我们的可学习拓扑特征可以提供系统发育树的有效结构信息，自动适应不同的下游任务，而无需领域专业知识。我们在模拟数据树概率估计任务和挑战真实数据变分贝叶斯系统发育推理问题的基准上证明了我们的方法的有效性和效率。
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.