{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/differentiable-phylogenetics-via-hyperbolic","title":"Differentiable Phylogenetics via Hyperbolic Embeddings with Dodonaphy","arxiv_id":"2309.11732","date":"2023-09-21","proceeding":null,"authors":["Matthew Macaulay","Mathieu Fourment"],"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-art methods. However, geometric frustrations of the embedding locations produce local optima that pose a challenge for optimisation. Availability: Dodonaphy is freely available on the web at www.https://github.com/mattapow/dodonaphy. 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