Master Imalis 2

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) From September 30th to October 4th, 2024.

Assessment

The evaluation of the course 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.

Course material

The schedule and all slides will be made available on Institut Curie’s training website (https://training.institut-curie.org/courses/sysbiocancer2024).

Suggested readings

Barillot E, Calzone L, Hupe P, Vert J-P, Zinovyev A (2013). Computational Systems Biology of Cancer. CRC Press.

This course offers a broad overview of microbial functions and ecosystems studied in the specific context of anthropogenic climate change. Based on recent research in the field, it delineates how microbial life and its perturbations influence climate or modulate the consequences of its change, and reciprocally how microorganisms respond to global changes. It aims at providing concrete tools to understand why microbial diversity needs to be taken into account as a primordial, though unseen, ingredient of climate diagnosis, policies and action.