EmbryoNet: using deep learning to link embryonic phenotypes to signaling pathways.

ABSTRACT: Evolutionarily conserved signaling pathways are essential for early embryogenesis, and reducing or abolishing their activity leads to characteristic developmental defects. Classification of phenotypic defects can identify the underlying signaling mechanisms, but this requires expert knowledge and the classification schemes have not been standardized. Here we use a machine learning approach for automated phenotyping to train a deep convolutional neural network, EmbryoNet, to accurately identify zebrafish signaling mutants in an unbiased manner. Combined with a model of time-dependent developmental trajectories, this approach identifies and classifies with high precision phenotypic defects caused by loss of function of the seven major signaling pathways relevant for vertebrate development. O

SUBMITTER: Capek D 

PROVIDER: S-EPMC10250202 | biostudies-literature | 2023 Jun

REPOSITORIES: biostudies-literature