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developed will be based on pseudonymization, anonymization, and synthetic data generation. Using real health data as a source of information, we aim to create test datasets and statistical models
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models using sophisticate genetic tools, in vivo time-lapse imaging and multi-omics methods to decipher the underpinning mechanisms of regeneration. Our findings provide new targetable mechanisms
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the entire population. The project utilises advanced statistical methods such as multilevel models (mixed models), fixed-effects models, cluster analysis, and sequence analysis. The selected researcher is
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that currently suffer from observation gaps. By filling the observation gaps, we obtain a database essential to disentangle source and transport processes of SEPs and shed light on the underlying SEP acceleration