This work introduces MAGIK, a geometric deep learning framework for characterizing dynamic properties from time-lapse microscopy. MAGIK exploits geometric deep learning capability to capture the full spatiotemporal complexity of biological experiments using Graph Attention Networks. By processing object features with geometric priors, the neural network is capable of performing multiple tasks, from linking coordinates into trajectories to inferring local and global dynamic properties of the biological system. We demonstrate the flexibility and reliability of MAGIK by applying it to real and simulated data corresponding to a broad range of biological experiments.
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