用小规模神经网络逼近进化距离函数，实现高效精准的物种演化关系推断。
On the Approximation of Phylogenetic Distance Functions by Artificial Neural Networks
- 设计极简神经网络架构，拟合经典进化距离函数。
- 在多种分子进化模型下学习距离，性能接近主流推断方法。
- 计算开销小，适合大规模物种与基因数据处理。
推断生物样本间的系统发育关系是现代生物学的核心问题。尽管基于距离的层次聚类算法曾在此任务中取得成功，但已被基于复杂分子进化模型的贝叶斯和最大似然搜索方法取代。本文描述了能够逼近经典系统发育距离函数的最小神经网络架构，并明确了在多种分子进化模型下学习距离所需的关键条件。与基于模型的方法（以及近期提出的无模型卷积和Transformer网络）相比，这些架构计算开销小，可扩展至大量分类单元和分子特征。所学距离函数泛化能力强，只要训练数据充分，性能可媲美当前最优推断方法。
原文摘要 · Abstract (English)
Inferring the phylogenetic relationships among a sample of organisms is a fundamental problem in modern biology. While distance-based hierarchical clustering algorithms achieved early success on this task, these have been supplanted by Bayesian and maximum likelihood search procedures based on complex models of molecular evolution. In this work we describe minimal neural network architectures that can approximate classic phylogenetic distance functions and the properties required to learn distances under a variety of molecular evolutionary models. In contrast to model-based inference (and recently proposed model-free convolutional and transformer networks), these architectures have a small computational footprint and are scalable to large numbers of taxa and molecular characters. The learned distance functions generalize well and, given an appropriate training dataset, achieve results comparable to state-of-the art inference methods.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。