PhyloGFN: Phylogenetic inference with generative flow networks

Official repository of PhyloGFN: Phylogenetic inference with generative flow networks (ICLR 2024)

Mingyang Zhou, Zichao Yan, Elliot Layne, Nikolay Malkin, Dinghuai Zhang, Moksh Jain, Mathieu Blanchette, Yoshua Bengio

We design a GFlowNet based generative model for phylogenetic inference, achieving strong results in both Bayesian and parsimony-based phylogenetic inference.

Citation

@inproceedings{
    zhou2024phylogfn,
    title={Phylo{GFN}: Phylogenetic inference with generative flow networks},
    author={Ming Yang Zhou and Zichao Yan and Elliot Layne and Nikolay Malkin and Dinghuai Zhang and Moksh Jain and Mathieu Blanchette and Yoshua Bengio},
    booktitle={The Twelfth International Conference on Learning Representations},
    year={2024},
    url={https://openreview.net/forum?id=hB7SlfEmze}
}

Environment Setup

conda create -n phylogfn python=3.10
conda activate phylogfn
conda install pytorch torchvision torchaudio pytorch-cuda=11.8 -c pytorch -c nvidia
conda install anaconda::docopt
conda install etetoolkit::ete3
conda install matplotlib tqdm dill fvcore iopath docopt

Usage

Our latest progress with continuous branch lengths modeling is implemented. To train a phyogfn model

python train.py <cfg_path> <sequences_path> <output_path> [--nb_device=<device_num>] [--quiet] [--amp]

• Example sequences datasets DS1-DS8 are stored in the folder dataset/benchmark_datasets
• Example training <cfg_path> are stored in the folder src/configs/benchmark/dna_cfgs
  • Continuous branch lengths modeling configs are in the folder continuous_branch_lengths_modeling
  • Discrete branch lengths modeling configs are in the folder discrete_branch_lengths

PhyloGFN with continuous branch length modeling achieves SOTA MLL estimation performance.

	Training trajs	DS1	DS2	DS3	DS4	DS5	DS6	DS7	DS8	cfg/weights
PhyloGFN Full	3.20E+07	-7108.4 (0.04)	-26367.7 (0.04)	-33735.1 (0.02)	-13329.9 (0.09)	-8214.4 (0.16)	-6724.2 (0.10)	-37331.9 (0.14)	-8650.5 (0.05)	-
PhyloGFN 40%	1.28E+07	-7108.4 (0.05)	-26367.7 (0.05)	-33735.2 (0.04)	-13330.1 (0.07)	-8214.5 (0.14)	-6724.3 (0.10)	-37332.1 (0.27)	-8650.4 (0.16)	-
PhyloGFN 24%	7.68E+06	-7108.4 (0.05)	-26367.7 (0.02)	-33735.1 (0.07)	-13330.0 (0.08)	-8214.5 (0.13)	-6724.2 (0.21)	-37332.2 (0.26)	-8650.4 (0.15)	googledrive

For discrete branch lengths modeling, we suggest to use the config file with the following configurations:

	DS1	DS2	DS3	DS4	DS5	DS6	DS7	DS8
Bin size	0.001	0.004	0.004	0.002	0.002	0.001	0.001	0.001
Bin num	50	50	50	100	100	100	200	100

We will publish the optimized code for parsimony analysis in the near future. In the meantime, if you are interested, please refer to the supplementary materials to run parsimony analysis.

TODO list

• mutigpu training
• parsimony inference