LIS (UMR 7020), IBDM (UMR 7288), Turing Center For Living Systems, Aix-Marseille University, 13009, Marseille, France
Development. 2021 Jan 10;148(1):dev188474. doi: 10.1242/dev.188474.
Developmental biology has grown into a data intensive science with the development of high-throughput imaging and multi-omics approaches. Machine learning is a versatile set of techniques that can help make sense of these large datasets with minimal human intervention, through tasks such as image segmentation, super-resolution microscopy and cell clustering. In this Spotlight, I introduce the key concepts, advantages and limitations of machine learning, and discuss how these methods are being applied to problems in developmental biology. Specifically, I focus on how machine learning is improving microscopy and single-cell 'omics' techniques and data analysis. Finally, I provide an outlook for the futures of these fields and suggest ways to foster new interdisciplinary developments.
发育生物学随着高通量成像和多组学方法的发展，已经成为一门数据密集型科学。机器学习是一套通用的技术，可以通过图像分割、超分辨率显微镜和细胞聚类等任务，在最小的人工干预下帮助理解这些大型数据集。在这篇特写文章中，我介绍了机器学习的关键概念、优势和局限性，并讨论了这些方法如何应用于发育生物学中的问题。具体来说，我专注于机器学习如何改进显微镜和单细胞“组学”技术和数据分析。最后，我对这些领域的未来进行了展望，并提出了促进新的跨学科发展的方法。
