Projects

Funded research projects at the Caravagna Lab

Research · Projects

Funded research

Our projects connect methodological development with biological questions, experimental design and clinical collaboration.

01 AIRC BRIDGE · 2025–26 AI for clonal evolution under therapy
Conceptual illustration of tumour clones evolving under treatment

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

This project develops learning strategies to connect genomic alterations, cellular phenotypes and therapeutic response. The objective is to identify the evolutionary changes that allow resistant populations to emerge and persist.

Funding
€100,000 · AIRC Foundation
Programme
BRIDGE Grant · Computational Biology
Leadership
Giulio Caravagna · Principal Investigator
Team
Cancer Data Science Laboratory

Example papers changes on reload

02 PRIN · 2023–25 Machine learning for single-cell long-read sequencing
Conceptual illustration of single cells, long sequencing reads and machine learning

Algorithms that learn tumour structure from long molecular measurements collected one cell at a time.

The project combines single-cell resolution with long-read sequencing to observe linked molecular events that shorter assays cannot resolve. We develop models for noisy, heterogeneous measurements and use them to reconstruct cellular populations and their evolutionary relationships.

Funding
€250,000 total · approximately €140,000 to the lab
Programme
PRIN · Italian Ministry of University and Research · PE6
Leadership
Giulio Caravagna · Principal Investigator
Co-PI
Alberto Cazzaniga · Area Science Park

Example papers changes on reload

03 AIRC MFAG · 2021–25 Genotype, phenotype and therapeutic response
Conceptual illustration connecting tumour genotype, phenotype and treatment response

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

This programme established our integrated approach to cancer evolution: combine sequencing, quantitative models and biological validation to understand how treatment reshapes heterogeneous tumours and selects resistant clones.

Funding
€500,000 · AIRC Foundation
Programme
My First AIRC Grant · Computational Biology
Leadership
Giulio Caravagna · Principal Investigator
Team
Cancer Data Science Laboratory

Example papers changes on reload