PubReading [144] - What machine learning can do for developmental biology - P. Villoutreix

<p><strong>Developmental biology</strong> 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 <strong>techniques</strong> 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 <strong>machine learning</strong>, 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 <strong>data analysis</strong>. Finally, I provide an outlook for the futures of these fields and suggest ways to foster new interdisciplinary developments.</p><p><em>oi:10.1242/dev.188474 - 2021</em></p>