小柯机器人

德国康斯坦茨大学Patrick Müller团队使用深度学习将胚胎表型与信号通路联系起来。相关论文于2023年5月8日在线发表在《自然—方法学》杂志上。

研究人员使用机器学习方法进行了自动表型分类，并训练一个深度卷积神经网络EmbryoNet，以无偏倚的方式准确识别斑马鱼的信号突变体。结合随时间变化的发育轨迹模型，这种方法可以高精度地识别和分类与脊椎动物发育有关的七种主要信号通路功能丧失所导致的表型缺陷。这个分类算法在发育生物学中有着广泛的应用，并能稳健地识别演化上相距甚远物种中的信号传导缺陷。此外，利用高通量药物筛选中的自动表型，研究人员表明EmbryoNet可以解决药物物质的作用机制。作为这项工作的一部分，研究人员免费提供了超过200万张用于训练和测试EmbryoNet图像。

据介绍，演化保守的信号通路对早期胚胎发育至关重要，减少或取消它们的活性会导致特征性发育缺陷。表型缺陷的分类可以确定潜在的信号机制，但这需要专业知识，而且分类方案还没有标准化。

附：英文原文

Title: EmbryoNet: using deep learning to link embryonic phenotypes to signaling pathways

Author: apek, Daniel, Safroshkin, Matvey, Morales-Navarrete, Hernn, Toulany, Nikan, Arutyunov, Grigory, Kurzbach, Anica, Bihler, Johanna, Hagauer, Julia, Kick, Sebastian, Jones, Felicity, Jordan, Ben, Mller, Patrick

Issue&Volume: 2023-05-08

Abstract: Evolutionarily conserved signaling pathways are essential for early embryogenesis, and reducing or abolishing their activity leads to characteristic developmental defects. Classification of phenotypic defects can identify the underlying signaling mechanisms, but this requires expert knowledge and the classification schemes have not been standardized. Here we use a machine learning approach for automated phenotyping to train a deep convolutional neural network, EmbryoNet, to accurately identify zebrafish signaling mutants in an unbiased manner. Combined with a model of time-dependent developmental trajectories, this approach identifies and classifies with high precision phenotypic defects caused by loss of function of the seven major signaling pathways relevant for vertebrate development. Our classification algorithms have wide applications in developmental biology and robustly identify signaling defects in evolutionarily distant species. Furthermore, using automated phenotyping in high-throughput drug screens, we show that EmbryoNet can resolve the mechanism of action of pharmaceutical substances. As part of this work, we freely provide more than 2million images that were used to train and test EmbryoNet.

DOI: 10.1038/s41592-023-01873-4

Source: https://www.nature.com/articles/s41592-023-01873-4

Nature Methods：《自然—方法学》，创刊于2004年。隶属于施普林格·自然出版集团，最新IF：47.99
官方网址：https://www.nature.com/nmeth/
投稿链接：https://mts-nmeth.nature.com/cgi-bin/main.plex