Discovering Novel Biological Traits From Images Using Phylogeny-Guided Neural Networks
This addresses a label-scarce challenge in biology for trait discovery, offering a novel computational method.
The paper tackles the problem of discovering evolutionary traits from images without labeled data by introducing Phylo-NN, which encodes images into quantized feature vectors guided by phylogeny, and demonstrates effectiveness in tasks like species image generation and translation for fish species.
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.