What machine learning can do for developmental biology

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.

Automated summary

Developmental biology has evolved into a data-intensive science, and machine learning is helping make sense of large datasets with minimal human intervention. Machine learning techniques are being applied to enhance microscopy, single-cell 'omics' techniques, and data analysis in developmental biology, shaping the future of interdisciplinary developments in these fields.