Aims
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 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 data integration and predictive modelling 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 machine learning methods for understanding the heterogeneity of tumours and applications in personalised treatment schemes development.
Themes
Speakers will expose various approaches for omics, imaging, and clinical data analysis, as well as interpretation combining signalling networks together with multi-scale datasets. They will further cover drug sensitivity prediction algorithms, biomarkers and cancer drivers identification, patient stratification approaches, as well as application of mathematical modelling and image analysis in cancer with focus on AI/ML approaches
Schedule
The course (granting 3 ECTS for IMaLiS students) is organized at Institut Curie (Paris) over a the last full week of September 2023.
The course week will be followed by a two week-long computational project (granting 6 ECTS), based on the content of the article presented during the first week.
Assessment
The evaluation of the first week is based on the oral presentation of an article related to the topics of the course. Article assignment is organised before the start of the course, while the presentations take place on the afternoons of Thursday and Friday during the course week.
The evaluation of the projects is based on the production of a computational notebook (in python and/or R) and on an oral presentation and discussion at the end of the last week.
Course material
The schedule and all slides will be made available on Institut Curie’s training website (https://training.institut-curie.org/courses/sysbiocancer2023).
Suggested readings
Barillot E, Calzone L, Hupe P, Vert J-P, Zinovyev A (2013). Computational Systems Biology of Cancer. CRC Press.
- Enseignant: Denis THIEFFRY