What machine learning can do for developmental biology

Paul Villoutreix (1, 2)

1 LIS - Laboratoire d'Informatique et des Systèmes (LIS) (Marseille, Toulon)
2 IBDM - Institut de Biologie du Développement de Marseille

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, superresolution 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.

Mots clés: Artificial intelligence; Big data; Machine learning; Neural networks

Domaines: Biologie du développement; Apprentissage [cs.LG]; Machine Learning [stat.ML]

Paul Villoutreix. What machine learning can do for developmental biology. Development (Cambridge, England), 2021, 148 (1), pp.dev188474. ⟨10.1242/dev.188474⟩. ⟨hal-03200144⟩