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 https://github.com/zmy1116/phylogfn.
Cite
@article{arxiv.2310.08774,
title = {PhyloGFN: Phylogenetic inference with generative flow networks},
author = {Mingyang Zhou and Zichao Yan and Elliot Layne and Nikolay Malkin and Dinghuai Zhang and Moksh Jain and Mathieu Blanchette and Yoshua Bengio},
journal= {arXiv preprint arXiv:2310.08774},
year = {2024}
}