Discovering Novel Biological Traits From Images Using Phylogeny-Guided
  Neural Networks
Mohannad Elhamod Mridul Khurana Harish Babu Manogaran Josef C. Uyeda Meghan A. Balk Wasila Dahdul Yasin Bakış Henry L. Bart Jr. Paula M. Mabee Hilmar Lapp James P. Balhoff Caleb Charpentier David Carlyn Wei-Lun Chao Charles V. Stewart Daniel I. Rubenstein Tanya Berger-Wolf Anuj Karpatne
Discovering evolutionary traits that are heritable across species on the tree
of life (also referred to as a phylogenetic tree) is of great interest to
biologists to understand how organisms diversify and evolve. However, the
measurement of traits is often a subjective and labor-intensive process, making
trait discovery a highly label-scarce problem. We present a novel approach for
discovering evolutionary traits directly from images without relying on trait
labels. Our proposed approach, Phylo-NN, encodes the image of an organism into
a sequence of quantized feature vectors -- or codes -- where different segments
of the sequence capture evolutionary signals at varying ancestry levels in the
phylogeny. We demonstrate the effectiveness of our approach in producing
biologically meaningful results in a number of downstream tasks including
species image generation and species-to-species image translation, using fish
species as a target example.