Computational Systems Biology of Cancer

7th edition - Spatial multimodal data analysis: when omics meet images

The course will gather leading speakers from different fields in cancer systems biology, 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 molecular data, further associating with clinical data.

More specific topics include multimodal genomic data integration and analysis, drug sensitivity prediction algorithms, identification of biomarkers and cancer drivers, patient stratification, and applications of mathematical modelling and image analysis in cancer.

You might also be interested in our satellite symposium on Multiple roles of RNAs and their disregulations in cancer, co-organised with the Cancer Science Institute, National University of Singapore.

Keynote speakers

Paul MACKLIN - US

Samantha MORRIS - US

Cours en English

3 ECTS

Registration deadline

August 5th 2024

Speakers
Plus d'informations
Documents

Intervenants

Fatima AL-SHAHROUR - ES
Emmanuel BARILLOT - FR
Giovanni CIRIELLO - CH
Leanne DE KONING - FR
Elisa FICARRA - IT
Åsmund FLOBAK - NO
Laurent GATTO - BE
Lisbeth GERIS - BE
Connie R. JIMENEZ - NL
Marta LOVINO - IT
Arnau MONTAGUD - ES
Vera PANCALDI - FR
Julio SAEZ-RODRIGUEZ - DE
Nicolas SERVANT - FR
Oznur TASTAN - TR
Kim THRANE - SE
Britta VELTEN - DE
Thomas WALTER - FR

Plus d’informations

Objectifs

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 understand and use multimodal integration approaches to efficiently exploit the various kinds data accumulating in most biological or medical labs.

The course will review current methods and tools for the analysis and interpretation of mutltimodal genomic data, with a special focus on recent spatial transcriptomics and proteomics, along with concrete applications related to cancer.

In particular, the course will showcase computational methods enabling us to deepen our understanding of the heterogeneity of tumours, to leverage multimodal integration of clinical and omics data, and to design personalized treatment schemes.