The goal of the course is to promote computational approaches in biological and clinical laboratories. We aim to help participants understand and utilise multimodal integration approaches to effectively leverage the various types of data accumulating in most biological or medical labs.

The course will review current methods and tools for analysing and interpreting multimodal genomic data, with a focus on natural language processing and network approaches, as well as concrete applications related to cancer.

In particular, the course will present computational methods that allow us to deepen our understanding of tumour heterogeneity, make use of multimodal integration of clinical and omics data, and design personalised treatment regimens.

The invited speakers will expose various approaches for omics, imaging, clinical data analysis and interpretation, combining signalling networks together with multi-scale molecular data, further associating with clinical data.

They will further review current methods and tools for the analysis and interpretation of pangenomic data, with a special focus on recent spatial transcriptomics and proteomics, along with concrete applications related to cancer.

More specific topics include multimodal data integration and analysis, drug sensitivity prediction algorithms, identification of biomarkers and cancer drivers, patient stratification, and applications of mathematical modelling and image analysis in cancer.