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- W4385568032 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" @default.
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- W4385568032 date "2023-08-04" @default.
- W4385568032 modified "2023-09-27" @default.
- W4385568032 title "Discovering Novel Biological Traits From Images Using Phylogeny-Guided Neural Networks" @default.
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- W4385568032 doi "https://doi.org/10.1145/3580305.3599808" @default.
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