9th course on Computational Systems Biology of Cancer

Generative artificial intelligence and network modelling for cancer diagnosis and treatment

The diversity across tumours from different patients and even across cancer cells from the same patient tremendously complicates the development of treatments. ‘Personalized’ or ‘precision’ medicine aims to overcome this problem by finding a tailored treatment regimen for each patient according to the individual genetic background and tumour molecular profile. This ambitious goal requires proper molecular characterization of patient cancer using high-throughput technologies and advanced imaging technologies. Although cancer multi-scale data are becoming available, these still need to be fully exploited to elucidate the underlying mechanisms and thereby inform patient stratification and treatment. 

Since multiple views for the same patients can provide complementary information, multimodal integrative analysis of omics and clinical data has great potential to elucidate the molecular underpinning of cancer types.  Multimodal integration of disease data holds great promise in uncovering intricate relationships among molecular mechanisms collectively contributing to the disease. 

OBJECTIVES

The goal of the course is to promote computational approaches in biological and clinical laboratories. We aim to help participants understand and utilize 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 generative artificial intelligence and network approaches, as well as concrete applications related to cancer.

Keynote speaker