Macaulay Matthew, Fourment Mathieu
Australian Institute for Microbiology & Infection, University of Technology Sydney, Ultimo, NSW 2007, Australia.
Bioinform Adv. 2024 Jun 19;4(1):vbae082. doi: 10.1093/bioadv/vbae082. eCollection 2024.
Navigating the high dimensional space of discrete trees for phylogenetics presents a challenging problem for tree optimization. To address this, hyperbolic embeddings of trees offer a promising approach to encoding trees efficiently in continuous spaces. However, they require a differentiable tree decoder to optimize the phylogenetic likelihood. We present soft-NJ, a differentiable version of neighbour joining that enables gradient-based optimization over the space of trees.
We illustrate the potential for differentiable optimization over tree space for maximum likelihood inference. We then perform variational Bayesian phylogenetics by optimizing embedding distributions in hyperbolic space. We compare the performance of this approximation technique on eight benchmark datasets to state-of-the-art methods. Results indicate that, while this technique is not immune from local optima, it opens a plethora of powerful and parametrically efficient approach to phylogenetics tree embeddings.
Dodonaphy is freely available on the web at https://www.github.com/mattapow/dodonaphy. It includes an implementation of soft-NJ.
在系统发育学中，在离散树的高维空间中进行导航对于树的优化来说是一个具有挑战性的问题。为了解决这个问题，树的双曲嵌入提供了一种在连续空间中有效编码树的有前景的方法。然而，它们需要一个可微的树解码器来优化系统发育似然性。我们提出了soft-NJ，这是一种邻接法的可微版本，能够在树空间上进行基于梯度的优化。
我们展示了在树空间上进行可微优化以进行最大似然推断的潜力。然后，我们通过在双曲空间中优化嵌入分布来执行变分贝叶斯系统发育学。我们将这种近似技术在八个基准数据集上的性能与现有最先进的方法进行了比较。结果表明，虽然这种技术无法避免局部最优，但它为系统发育树嵌入开辟了大量强大且参数高效的方法。
Dodonaphy可在https://www.github.com/mattapow/dodonaphy网站上免费获取。它包括soft-NJ的实现。
