Learning From an Artificial Neural Network in Phylogenetics

We show that an iterative ansatz of deep learning and human intelligence guided simplification may lead to surprisingly simple solutions for a difficult problem in phylogenetics. Distinguishing Farris and Felsenstein trees is a longstanding problem in phylogenetic tree reconstruction. The Artificial Neural Network F-zoneNN solves this problem for 4-taxon alignments evolved under the Jukes-Cantor model. It distinguishes between Farris and Felsenstein trees, but owing to its complexity, lacks transparency in its mechanism of discernment. Based on the simplification of F-zoneNN and alignment properties we constructed the function FarFelDiscerner. In contrast to F-zoneNN, FarFelDiscerner's decision process is understandable. Moreover, FarFelDiscerner is significantly simpler than F-zoneNN. Despite its simplicity this function infers the tree-type almost perfectly on noise-free data, and also performs well on simulated noisy alignments of finite length. We applied FarFelDiscerner to the historical Holometabola alignments where it places Strepsiptera with beetles, concordant with the current scientific view.