Global strategy of active machine learning for complex systems: Embryogenesis application on cell division detection

The intrinsic complexity of biological systems creates huge amounts of unlabeled experimental data. The exploitation of such data can be achieved by performing active machine learning accompanied by a high-level symbolic expert who defines categories and their best boundaries using as little data as possible. We present a global strategy for designing active machine learning methods suited for the observation and analysis of complex systems, such as embryonic development. We developed a procedure that uses all available knowledge, whether gathered manually or automatically, and is able to readjust when new data is provided. We show that it is a powerful method for the investigation of the morphogenetic features of embryogenesis and specifically mitosis detection. It will make possible to properly reconstruct the in vivo cell morphodynamics, a main challenge of the post-genomic era.