Discovering Novel Biological Traits From Images Using Phylogeny-Guided Neural Networks-猫眼课题宝

DOI：

10.1145/3580305.3599808

发表时间：

2023-06

期刊：

Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining

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其他

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作者：

Mohannad Elhamod;Mridul Khurana;Harish Babu Manogaran;J. Uyeda;M. Balk;W. Dahdul;Yasin Bakics;H. Bart;Paula M. Mabee;H. Lapp;J. Balhoff;Caleb Charpentier;David Carlyn;Wei-Lun Chao;Chuck Stewart;D. Rubenstein;T. Berger-Wolf;A. Karpatne

发现生命树（也称为系统发育树）上跨物种遗传的进化特征对生物学家了解生物体如何多样化和进化具有极大的兴趣。然而，特质的测量往往是一个主观和劳动密集型的过程，使特质发现一个高度标签稀缺的问题。我们提出了一种新的方法，发现进化特征直接从图像，而不依赖于性状标签。我们提出的方法，Phylo-NN，将生物体的图像编码成一系列量化的特征向量-或代码-其中序列的不同片段在不同的祖先水平上捕获进化信号。我们证明了我们的方法的有效性，在产生生物意义的结果，在一些下游任务，包括物种图像生成和物种到物种的图像翻译，使用鱼类作为目标的例子

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

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

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