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 the course is to promote better use of computational approaches into biological labs and to clinics. We aim to help participants to improve interpretation of various types of data accumulated in the labs using multi-modal data integration approaches.

Registration site: 




Clinical research

Prior knowledge


Spatial transcriptomics

Imaging data


Clinical data

Text mining



Mathematical modelling

Predictive models for various multi-omics data types

Treatment response prediction and prognosis

Partners and Sponsors


  • Acceptance Notification
    5 August 2022
  • Application Closing
    30 July 2022

FEBS has twin commitments to high-quality publications and the promotion of molecular biosciences. As a charitable academic organization,
FEBS uses income from the journals to fund its diverse activities, including support for FEBS Advanced Courses.