通过双曲嵌入与 Dodonaphy 的可微分系统学

IF 2.4 Q2 MATHEMATICAL & COMPUTATIONAL BIOLOGY
Bioinformatics advances Pub Date : 2024-06-19 eCollection Date: 2024-01-01 DOI:10.1093/bioadv/vbae082
Matthew Macaulay, Mathieu Fourment
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引用次数: 0

摘要

动机在离散树的高维空间中进行系统发育导航是树优化的一个挑战性问题。为了解决这个问题,树的双曲嵌入为在连续空间中有效编码树提供了一种很有前景的方法。然而,它们需要一个可微分的树解码器来优化系统发育似然。我们提出了软邻接(soft-NJ),这是邻接的可微分版本,可以在树的空间中进行基于梯度的优化:结果:我们说明了在最大似然推断中对树空间进行可微分优化的潜力。然后,我们通过优化双曲空间中的嵌入分布来执行变异贝叶斯系统发育学。我们在八个基准数据集上比较了这种近似技术与最先进方法的性能。结果表明,虽然这种技术无法避免局部最优,但它通过树嵌入为系统发育开辟了大量功能强大、参数高效的方法:Dodonaphy 可在 https://www.github.com/mattapow/dodonaphy 网站上免费获取。可用性和实现:Dodonaphy 可在网上免费获取,网址是 。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Differentiable phylogenetics via hyperbolic embeddings with Dodonaphy.

Motivation: 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.

Results: 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 via tree embeddings.

Availability and implementation: Dodonaphy is freely available on the web at https://www.github.com/mattapow/dodonaphy. It includes an implementation of soft-NJ.

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CiteScore
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