Dinnage Russell, Kleineberg Marian
Department of Biological Sciences, Institute of Environment, Florida International University, Miami, Florida, United States of America.
Institute for Applied Ecology, University of Canberra, Canberra, Australia.
PLoS Comput Biol. 2025 Mar 17;21(3):e1012887. doi: 10.1371/journal.pcbi.1012887. eCollection 2025 Mar.
Data on the three dimensional shape of organismal morphology is becoming increasingly available, and forms part of a new revolution in high-throughput phenomics that promises to help understand ecological and evolutionary processes that influence phenotypes at unprecedented scales. However, in order to meet the potential of this revolution we need new data analysis tools to deal with the complexity and heterogeneity of large-scale phenotypic data such as 3D shapes. In this study we explore the potential of generative Artificial Intelligence to help organize and extract meaning from complex 3D data. Specifically, we train a deep representational learning method known as DeepSDF on a dataset of 3D scans of the bills of 2,020 bird species. The model is designed to learn a continuous vector representation of 3D shapes, along with a 'decoder' function, that allows the transformation from this vector space to the original 3D morphological space. We find that approach successfully learns coherent representations: particular directions in latent space are associated with discernible morphological meaning (such as elongation, flattening, etc.). More importantly, learned latent vectors have ecological meaning as shown by their ability to predict the trophic niche of the bird each bill belongs to with a high degree of accuracy. Unlike existing 3D morphometric techniques, this method has very little requirements for human supervised tasks such as landmark placement, increasing it accessibility to labs with fewer labour resources. It has fewer strong assumptions than alternative dimension reduction techniques such as PCA. Once trained, 3D morphology predictions can be made from latent vectors very computationally cheaply. The trained model has been made publicly available and can be used by the community, including for finetuning on new data, representing an early step toward developing shared, reusable AI models for analyzing organismal morphology.
关于生物体形态三维形状的数据越来越容易获取，并且构成了高通量表型组学新革命的一部分，有望帮助以前所未有的规模理解影响表型的生态和进化过程。然而，为了充分发挥这场革命的潜力，我们需要新的数据分析工具来处理大规模表型数据（如三维形状）的复杂性和异质性。在本研究中，我们探索生成式人工智能在帮助整理和从复杂三维数据中提取意义方面的潜力。具体而言，我们在2020种鸟类喙部的三维扫描数据集上训练了一种名为深度符号距离函数（DeepSDF）的深度表征学习方法。该模型旨在学习三维形状的连续向量表示以及一个“解码器”函数，该函数允许从这个向量空间转换到原始的三维形态空间。我们发现该方法成功地学习到了连贯的表示：潜在空间中的特定方向与可辨别的形态学意义（如伸长、变平等等）相关联。更重要的是，学习到的潜在向量具有生态意义，这体现在它们能够高度准确地预测每只喙所属鸟类的营养生态位。与现有的三维形态测量技术不同，这种方法对地标放置等人工监督任务的要求非常少，增加了资源较少的实验室使用它的便利性。与主成分分析（PCA）等替代降维技术相比，它的强假设更少。一旦训练完成，从潜在向量进行三维形态预测的计算成本非常低。训练好的模型已公开可用，社区可以使用，包括在新数据上进行微调，这代表了朝着开发用于分析生物体形态的共享、可重复使用人工智能模型迈出的早期一步。
