Discovering Novel Biological Traits From Images Using Phylogeny-Guided Neural Networks

• URL: http://arxiv.org/abs/2306.03228v1
• Date: Mon, 5 Jun 2023 20:22:05 GMT
• Title: Discovering Novel Biological Traits From Images Using Phylogeny-Guided Neural Networks
• Authors: Mohannad Elhamod, Mridul Khurana, Harish Babu Manogaran, Josef C. Uyeda, Meghan A. Balk, Wasila Dahdul, Yasin Bak{\i}\c{s}, 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
• Abstract summary: 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. We demonstrate the effectiveness of our approach in producing biologically meaningful results in a number of downstream tasks.
• Score: 10.372001949268636
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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