利用生命之树对扩散模型进行层级条件化以研究物种演化
Hierarchical Conditioning of Diffusion Models Using Tree-of-Life for Studying Species Evolution
AI总结：
本文提出Phylo-Diffusion框架，将生命之树的层级嵌入用于条件化扩散模型，并通过性状掩蔽与交换实验，从图像中可视化并揭示鱼类和鸟类的演化性状变异机制。
摘要：
生物学中的一个核心问题是理解生物体如何通过在生命之树上获得物种可观察特征或性状的变异来演化并适应其环境。随着生物学领域大规模图像库的日益普及以及生成建模的最新进展，出现了从图像中自动加速发现演化性状的机会。为了实现这一目标，我们引入了Phylo-Diffusion，这是一个新颖的框架，用于以层级嵌入（HIER-Embeds）形式表示的谱系知识来条件化扩散模型。我们还提出了两个新实验来扰动Phylo-Diffusion的嵌入空间：性状掩蔽和性状交换，其灵感来源于基因敲除和基因编辑/交换的对应实验。我们的工作代表了生成建模中一种新颖的方法论进展，即利用基于树的知识来结构化扩散模型的嵌入空间。我们的工作还通过使用生成模型直接从图像中可视化演化变化，开辟了演化生物学研究的新篇章。我们通过实验证明了Phylo-Diffusion在捕捉鱼类和鸟类有意义的性状变异方面的有效性，揭示了关于其演化生物学机制的新见解。
英文摘要：
A central problem in biology is to understand how organisms evolve and adapt to their environment by acquiring variations in the observable characteristics or traits of species across the tree of life. With the growing availability of large-scale image repositories in biology and recent advances in generative modeling, there is an opportunity to accelerate the discovery of evolutionary traits automatically from images. Toward this goal, we introduce Phylo-Diffusion, a novel framework for conditioning diffusion models with phylogenetic knowledge represented in the form of HIERarchical Embeddings (HIER-Embeds). We also propose two new experiments for perturbing the embedding space of Phylo-Diffusion: trait masking and trait swapping, inspired by counterpart experiments of gene knockout and gene editing/swapping. Our work represents a novel methodological advance in generative modeling to structure the embedding space of diffusion models using tree-based knowledge. Our work also opens a new chapter of research in evolutionary biology by using generative models to visualize evolutionary changes directly from images. We empirically demonstrate the usefulness of Phylo-Diffusion in capturing meaningful trait variations for fishes and birds, revealing novel insights about the biological mechanisms of their evolution.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
