Spatial multimodal data analysis: when omics meet images (Computational Systems Biology of Cancer)

The course will gather leading speakers from different fields in cancer systems biology, in cancer research and in clinics. 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.

More specific topics include multimodal genomic 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.

 

Objectives

The objective of the course is to promote better integration of computational approaches into biological and clinical labs and to clinics. We aim to help participants to understand and use  multimodal integration approaches to efficiently exploit the various kinds data accumulating in most biological or medical labs.

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

In particular, the course will showcase computational methods enabling us to deepen our understanding of the heterogeneity of tumours, to leverage multimodal integration of clinical and omics data, and to design personalized treatment schemes.