ARTree: A Deep Autoregressive Model for Phylogenetic Inference
This addresses the need for efficient phylogenetic inference methods in computational biology, representing an incremental improvement by replacing heuristic features with learnable ones.
The paper tackles the problem of designing flexible probabilistic models for phylogenetic tree topologies by proposing ARTree, a deep autoregressive model using graph neural networks, which avoids hand-engineered features and shows effectiveness on real data benchmarks.
Designing flexible probabilistic models over tree topologies is important for developing efficient phylogenetic inference methods. To do that, previous works often leverage the similarity of tree topologies via hand-engineered heuristic features which would require pre-sampled tree topologies and may suffer from limited approximation capability. In this paper, we propose a deep autoregressive model for phylogenetic inference based on graph neural networks (GNNs), called ARTree. By decomposing a tree topology into a sequence of leaf node addition operations and modeling the involved conditional distributions based on learnable topological features via GNNs, ARTree can provide a rich family of distributions over the entire tree topology space that have simple sampling algorithms and density estimation procedures, without using heuristic features. We demonstrate the effectiveness and efficiency of our method on a benchmark of challenging real data tree topology density estimation and variational Bayesian phylogenetic inference problems.