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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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-scale omics datasets. The project aims to identify molecular factors influencing disease development and investigate how this understanding can be utilized in advancing treatments for immune-mediated
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aims to train experts and leaders in the broad field of immunology who will advance research to address the needs in the prevention and treatment of immune-mediated diseases. Its goals are breakthrough
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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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shocks, which are considered the main drivers of SEP events. AIPAD supports open science and will produce innovative datasets, event catalogs, and advanced AI algorithms for the scientific community