About the EMBO-FEBS Lecture Course
The diversity across tumors from different patients and even across cancer cells from the same patient makes the picture very complex. The idea of ‘personalized’ or ‘precision’ medicine has been suggested, aiming to find tailored treatment regimen for each patient according to the individual genetic background and tumor molecular profile. This attempt is achievable thanks to sufficient molecular characterization of cancers accumulated using high-throughput technologies and advanced imaging technologies. However, despite availability of cancer multi-scale data, they are not fully exploited to provide the clue on deregulated mechanisms that would guide better patients stratification and to specific treatment in cancer.
The objective of this EMBO|FEBS Lecture Course is to promote better integration of computational approaches into biological and clinical labs and to clinics. We aim to help participants to improve interpretation and use of multi-scale data that nowadays are accumulated in any biological or medical lab. This year, the course will particularly focus on Artificial Intelligence (AI) and Machine Learning (ML) approaches in cancer research and in clinics. We will review current methods and tools for the analysis and interpretation of big data, along with concrete applications related to cancer. In particular, we will emphasize the role of AI/ML methods for understanding the heterogeneity of tumor and applications in personalized treatment schemes development.
We have invited leading speakers from different fields in cancer systems biology, especially from the field of Artificial Intelligence (AI) and Machine Learning (ML) 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 data and associating it to clinical data. In addition, drug sensitivity prediction algorithms, biomarkers and cancer drivers identification; patient stratification approaches; application of mathematical modelling and image analysis in cancer with focus on AI/ML approaches will be demonstrated.