Characterising genotype and phenotype clonal evolution of response to therapy with Artificial Intelligence

We lead a 5 years AIRC-funded MFAG project, where we use Artificial Intelligence to study clonal evolution under therapy, integrating longitudinal bulk and single-cell data of haematological cancers.

Machine Learning algorithms for single-cell and long-reads sequencing

We lead a 2 years MUR-funded PRIN project, where we develop new machine learning algorithms for both single-cell and long-reads sequencing technologies.

The EVOverse

With the Sottoriva lab (Human Technopole) we have established a collaborative project to develop new Artificial Intelligence and population genetics methods to measure clonal evolution from cancer sequencing data.


Single-cell cancer evolution in the clinic

We collaborate to this project to combine cancer evolution modelling, new single-cell approaches and novel microfluidic devices, as well as new data integration techniques, with the aim of providing a definitive single-cell portrait of tumor cells, before and after treatment.