Computational Genomics Laboratory
PI: Prof. Francesco Lescai
This laboratory is interested in modelling inflammation as a key predictor of phenotype trajectories. In order to tackle this challenge, we focus our activities on two main areas, tightly integrated with each other:
- computational genomics methods, to uncover patterns of genomic variation that explain individual differences in the inflammatory response
- the use of machine/deep learning to identify genomic and non-genomic parameters to predict an individual’s inflammatory response and how in turn this will affect the evolution of a phenotype or disease.