Cancer Data Science (CDS) Laboratory
Principal Investigator: Dr. Giulio Caravagna
The Cancer Data Science (CDS) Lab is a research laboratory based at the University of Trieste, Italy.
We develop computational tools to study tumours as an evolutionary process, approaching real-world biological questions using a variety of technologies. For example, among our contributions there are tools to measure clonal evolution and tumour heterogeneity from high-throughput whole-genome DNA sequencing data.
The CDS Lab has state-of-the-art expertise from basic bioinformatics to advanced Machine Learning and Artificial Intelligence (AI) applied to a variety assays and experimental designs. Thanks to a strong network of experimental collaborators, we also design our own experiments and generate new data.
A key strength of the lab is its heterogeneous composition, with backgrounds ranging from classical STEMS (computer science, physics, mathematics) to Life Sciences (molecular biology, genomics, biotechnology). We are therefore interested in the broad application of modern Machine Learning to quantitative biology.
Prospective students and potential collaborators interested in working with us can start from the information on these pages to get an idea of what we do.
Machine Learning expertise
probabilistic graphical models;
Bayesian and variational inference;
probabilistic programming (Pyro, STAN);
tools development (R and Python)
short-reads bulk whole-genome, whole-exome and targeted DNA sequencing;
tumour-matched normal and tumour-only designs, patient-derived organoids, cell lines;
single-sample, multi-region and longitudinal large-scale genomics;
single-cell RNA and single-cell ATAC sequencing;
spots-based spatial transcriptomics.
imaging for digital pathology.
2022 Laboratory showreel. 5' pitch covering ongoing research in the laboratory, usually helpful for students.
(for Data Scientists) Reconciling tumour genetic heterogeneity using computational methods and whole-genome sequencing. An introduction to computational approaches for tumour-evolution analyses (invited SISSA Data Science seminar, 2021).
(for Biologists) Reconciling intra-tumour genetic heterogeneity from whole-genome sequencing with computational methods. An introduction to tumour computational biology (invited talk, 4th p-care workshop on precision cancer medicine, 2022).
Where are we
- Department of Mathematics and Geosciences
- Office 332 (PI)
- Office 339 (laboratory)