Biomedical research is moving towards a multi-factorial analysis of biological processes. This approach is mainly applied in the medical field, where clinical, genomic and epidemiological data are intersected during the diagnostic and therapeutic pathway of a patient. The enormous technological progress made in the last decade has led to an innumerable and growing amount of biological data that can hardly be analysed by the simple human mind. The introduction of artificial intelligence (AI) in the analysis of complex data has revolutionised the ability to handle and interpret genomic and biological data. In recent years, numerous algorithms have been developed to facilitate the understanding of complex biological phenomena. However, the application of AI is not limited to the management of large amounts of data but has been successfully applied to the creation of predictive models of biological phenomena. Thanks to algorithms based on machine learning models, it is now possible to predict with a high degree of confidence the occurrence of a given biological phenomenon as a response to a specific drug in a subject.
Starting from these foundations, our team wants to deepen and implement the use of AI in the study of complex biological phenomena both in normal physiology and in human pathology. This group aims to use AI and machine learning-based algorithms to improve these analyses and generate data with effects on human health. (add Francesca’s part and any other more specific suggestions)
1) Facilitate dialogue between clinical/biological researchers and physical/computer scientists.
2) To disseminate the potential of AI in the clinical/biological field.
3) To improve knowledge of genomic and biological mechanisms by developing models for analysis and prediction.
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