PhyloGFN: Phylogenetic inference with generative flow networks

Abstract: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. Our code is available at this https URL.

Populations and Evolution (q-bio.PE); Machine Learning (cs.LG); Machine Learning (stat.ML)

arXiv:2310.08774 [q-bio.PE]

(or arXiv:2310.08774v3 [q-bio.PE] for this version)

https://doi.org/10.48550/arXiv.2310.08774 Focus to learn more arXiv-issued DOI via DataCite

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