Tree Containment is a fundamental problem in phylogenetics useful for
verifying a proposed phylogenetic network, representing the evolutionary
history of certain species. Tree Containment asks whether the given
phylogenetic tree (for instance, constructed from a DNA fragment showing
tree-like evolution) is contained in the given phylogenetic network. In the
general case, this is an NP-complete problem. We propose to solve it
approximately using Graph Neural Networks. In particular, we propose to combine
the given network and the tree and apply a Graph Neural Network to this
network-tree graph. This way, we achieve the capability of solving the tree
containment instances representing a larger number of species than the
instances contained in the training dataset (i.e., our algorithm has the
inductive learning ability). Our algorithm demonstrates an accuracy of over
$95\%$ in solving the tree containment problem on instances with up to 100
leaves.

通过使用图神经网络，我们提出了一种解决树包含问题的算法，能够在训练数据集之外的具有更多物种的实例上展示出超过 95% 的准确率。

使用图神经网络解决树包含问题

Solving the Tree Containment Problem Using Graph Neural Networks

Phylogenetics is a branch of computational biology that studies the
evolutionary relationships among biological entities. Its long history and
numerous applications notwithstanding, inference of phylogenetic trees from
sequence data remains challenging: the high complexity of tree space poses a
significant obstacle for the current combinatorial and probabilistic
techniques. In this paper, we adopt the framework of generative flow networks
(GFlowNets) to tackle two core problems in phylogenetics: parsimony-based and
Bayesian phylogenetic inference. Because GFlowNets are well-suited for sampling
complex combinatorial structures, they are a natural choice for exploring and
sampling from the multimodal posterior distribution over tree topologies and
evolutionary distances. We demonstrate that our amortized posterior sampler,
PhyloGFN, produces diverse and high-quality evolutionary hypotheses on real
benchmark datasets. PhyloGFN is competitive with prior works in marginal
likelihood estimation and achieves a closer fit to the target distribution than
state-of-the-art variational inference methods.

采用生成流网络的框架，本文研究在系统发生学中的两个核心问题：基于减少原则和贝叶斯方法的系统发生学推断。通过我们的前向后验采样器，PhyloGFN，我们展示了在真实基准数据集上产生多样化和高质量的进化假设。PhyloGFN 在边缘似然度估计方面与现有工作相媲美，并实现了更接近目标分布的拟合效果，超过了最先进的变分推断方法。

PhyloGFN: 基于生成流网络的系统发育推断

PhyloGFN: Phylogenetic inference with generative flow networks

Phylogenetics is now fundamental in life sciences, providing insights into
the earliest branches of life and the origins and spread of epidemics. However,
finding suitable phylogenies from the vast space of possible trees remains
challenging. To address this problem, for the first time, we perform both tree
exploration and inference in a continuous space where the computation of
gradients is possible. This continuous relaxation allows for major leaps across
tree space in both rooted and unrooted trees, and is less susceptible to
convergence to local minima. Our approach outperforms the current best methods
for inference on unrooted trees and, in simulation, accurately infers the tree
and root in ultrametric cases. The approach is effective in cases of empirical
data with negligible amounts of data, which we demonstrate on the phylogeny of
jawed vertebrates. Indeed, only a few genes with an ultrametric signal were
generally sufficient for resolving the major lineages of vertebrate. With
cubic-time complexity and efficient optimisation via automatic differentiation,
our method presents an effective way forwards for exploring the most difficult,
data-deficient phylogenetic questions.

我们采用连续空间中的树探索和推断方法来解决从大量可能的树中寻找合适的系统发育关系的问题，并通过在超度量空间中准确推断树和根来证明其有效性，并在数据不足的情况下成功地解决了关于有颌脊椎动物的系统发育关系。

跨越树空间：针对有根树和无根树的连续系统发育推断

Leaping through tree space: continuous phylogenetic inference for rooted  and unrooted trees

Stemmatology is a subfield of philology where one approach to understand the
copy-history of textual variants of a text (witnesses of a tradition) is to
generate an evolutionary tree. Computational methods are partly shared between
the sister discipline of phylogenetics and stemmatology. In 2022, a surveypaper
in nature communications found that Deep Learning (DL), which otherwise has
brought about major improvements in many fields (Krohn et al 2020) has had only
minor successes in phylogenetics and that "it is difficult to conceive of an
end-to-end DL model to directly estimate phylogenetic trees from raw data in
the near future"(Sapoval et al. 2022, p.8). In stemmatology, there is to date
no known DL approach at all. In this paper, we present a new DL approach to
placement of manuscripts on a stemma and demonstrate its potential. This could
be extended to phylogenetics where the universal code of DNA might be an even
better prerequisite for the method using sequence to sequence based neural
networks in order to retrieve tree distances.

本篇论文介绍了一个新的基于深度学习的方法来进行手稿放置，该方法可扩展至基因组的想法，并以序列到序列的神经网络模型来检索树距离。