Differentiable Phylogenetics via Hyperbolic Embeddings with Dodonaphy

Abstract

Abstract Motivation Navigating the high dimensional space of discrete trees for phylogenetics presents a challenging problem for tree optimisation. 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 optimise the phylogenetic likelihood. We present soft-NJ, a differentiable version of neighbour-joining that enables gradient-based optimisation over the space of trees. Results We illustrate the potential for differentiable optimisation over tree space for maximum likelihood inference. We then perform variational Bayesian phylogenetics by optimising 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 isn’t immune from local optima, it opens a plethora of powerful and parametrically efficient approach to phylogenetics via tree embeddings. Availability Dodonaphy is freely available on the web at www https://github.com/mattapow/dodonaphy. It includes an implementation of soft-NJ.