Solving the Tree Containment Problem Using Graph Neural Networks
This provides a scalable method for verifying phylogenetic networks, addressing a computational bottleneck in evolutionary biology.
The paper tackles the NP-complete Tree Containment problem in phylogenetics by using Graph Neural Networks to approximate solutions, achieving over 95% accuracy on instances with up to 100 leaves.
Tree Containment is a fundamental problem in phylogenetics useful for verifying a proposed phylogenetic network, representing the evolutionary history of certain species. Tree Containment asks whether the given phylogenetic tree (for instance, constructed from a DNA fragment showing tree-like evolution) is contained in the given phylogenetic network. In the general case, this is an NP-complete problem. We propose to solve it approximately using Graph Neural Networks. In particular, we propose to combine the given network and the tree and apply a Graph Neural Network to this network-tree graph. This way, we achieve the capability of solving the tree containment instances representing a larger number of species than the instances contained in the training dataset (i.e., our algorithm has the inductive learning ability). Our algorithm demonstrates an accuracy of over $95\%$ in solving the tree containment problem on instances with up to 100 leaves.