Recordings of the live talks from this EMBO|FEBS Lecture Course can be watched in the FEBS Network Room Cancer systems biology: promises of artificial intelligence.
The diversity across tumors from different patients and even across cancer cells from the same patient creates a very complex picture for disease management. The idea of ‘personalized’ or ‘precision’ medicine has been suggested, aiming to find a 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 stratification of patients and specific treatment in cancer.
The objective of this EMBO|FEBS Lecture Course was to promote better integration of computational approaches into biological and clinical labs and to clinics. We aimed 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 particularly focused on Artificial Intelligence (AI) and Machine Learning (ML) approaches in cancer research and in clinics. We reviewed current methods and tools for the analysis and interpretation of big data, along with concrete applications related to cancer. In particular, we emphasized the role of AI/ML methods for understanding the heterogeneity of tumors and applications in the development of personalized treatment schemes.
We invited leading speakers from different fields in cancer systems biology, especially from the field of AI and ML in cancer research and in clinics. The speakers presented various approaches for omics, imaging, clinical data analysis and interpretation, combining signaling networks together with multi-scale data and associating it to clinical data. In addition, the talks demonstrated drug-sensitivity prediction algorithms, biomarkers and cancer drivers identification; patient stratification approaches; and application of mathematical modelling and image analysis in cancer with a focus on AI/ML approaches. With the kind agreement of the invited speakers, recordings of the live talks are now available in a FEBS Network room for meeting participants to revisit and for viewing by those who did not attend the meeting.
Course history and environment
This course, which took place from September 27 to October 2, 2020, was organized by Institute Curie Training Unit & Research Unit of Bioinformatics and Computational Systems Biology of Cancer, and had support from EMBO and FEBS. This was the first real-time virtual EMBO|FEBS Lecture Course. You can find more information more information here .
The five-day virtual event included 25 international speakers and had around 200 participants from 31 countries. All five continents were represented. This virtual edition was accessible to a wider range of people worldwide, which had an extremely positive impact on the visibility of Institut Curie’s Research Center outside Europe.
Participants were carefully pre-selected according to their scientific profile from more than 300 applicants, and participation was free-of-charge. The virtual environment created by the organizers allowed a full-frame course to run, with activities that facilitated science discussion and professional connections between participants, speakers and organizers. Course activities were prepared via Microsoft Teams and participants could also interact through a Zapnito platform (a FEBS Network-related resource made available by FEBS).
This was a rich experience and we thank all the people that gave their time to make it happen! The feedback from the participants and speakers was very positive and encouraging. Lessons learnt during this event will allow us to adapt to a new reality and new possibilities, when it comes to the future of advance training in a research environment.