Hierarchy Aligned Commonality Through Prototypical Networks: Discovering Evolutionary Traits over Tree-of-Life

A grand challenge in biology is to discover evolutionary traits, which are features of organisms common to a group of species with a shared ancestor in the Tree of Life (also referred to as phylogenetic tree). With the recent availability of large-scale image repositories in biology and advances in the field of explainable machine learning (ML) such as ProtoPNet and other prototype-based methods, there is a tremendous opportunity to discover evolutionary traits directly from images in the form of a hierarchy of prototypes learned at internal nodes of the phylogenetic tree. However, current prototype-based methods are mostly designed to operate over a flat structure of classes and face several challenges in discovering hierarchical prototypes on a tree, including the problem of learning over-specific features at internal nodes in the tree. To overcome these challenges, we introduce the framework of Hierarchy aligned Commonality through Prototypical Networks (HComP-Net), which learns common features shared by all descendant species of an internal node and avoids the learning of over-specific prototypes. We empirically show that HComP-Net learns prototypes that are of high accuracy, semantically consistent, and generalizable to unseen species in comparison to baselines. While we focus on the biological problem of discovering evolutionary traits, our work can be applied to any domain involving a hierarchy of classes.