The conference is the final highlight of the Computational ONcology TRaining Alliance (CONTRA), an EU-funded Innovative Training Network. It aims to bring together investigators in the broader scope of oncology for talks and discussion on practical problems and latest research. We expect participants from different backgrounds including statistics, bioinformatics, imaging, and biomedical sciences.
The conference will involve a single track with 8 keynote speakers, contributed talks, and a poster session. The number of participants is limited to 100 to enable easy interactions and networking between participants.
Additionally, there will be a conference dinner and free afternoons for enjoying winter sports in the Swiss Alps together.
Where: Radisson Blu - Andermatt
Integration of different high-throughput molecular datasets in order to build comprehensive molecular models of disease that can help clinicians to provide better diagnoses and suggest personalized therapies.
Measurement of the patterns of clonal evolution that define carcinogenesis and develops novel mathematical tools for analysis and prediction. By characterising tumour evolution, he aims to find better ways to determine prognosis and more effective ways to treat cancers.
Exploration of the several highly effective pathways for DNA repair that have distinct specificities, and are evolutionary conserved despite partial redundancies.
Investigation of the genetic interactions of the intricate crosstalk between DNA damage and repair mechanisms.
Usage of high throughput sequencing to study the regulation of T and B cell receptor repertoire diversity with focus on the immune response to cancer, and to major pathogens including HIV and Mycobacterium tuberculosis.
Combination of single cell technologies, genomic datasets and machine learning techniques to address fundamental questions addressing regulatory cell circuits, cellular development, tumor immune eco-system, genotype to phenotype relations and precision medicine. .
Using Deep Learning and Computational Modeling in combination with a clinical perspective on cancer genomics, targeted treatment and immunotherapy to decipher cancer, with focus on gastrointestinal cancer, including cancer of the bowel, stomach, liver and pancreas.
Combination of approaches from information theory, statistical mechanics and machine learning to explore complex genetic and phenotypic datasets, focusing on the evolution of rapidly adapting systems such as pathogens and cancer, and how it is shaped by the interference with the immune system..
Investigating the molecular heterogeneity of tumor subtypes and the role of the cancer microenvironment in disease progression and drug resistance through the analysis of high-throughput whole genome data arising from molecular and genomic studies of cancer, with a particular focus on meta analysis and development of models that integrate of multiple sources of data.
|Niko Beerenwinkel||ETH Zurich|
|Jens Lagergren||KTH Stockholm|
|Ewa Szczurek||University of Warsaw|
|Nico Borgsmüller||ETH Zurich|
|Arhur Dondi||ETH Zurich|
|Iwona Zielinska||University of Warsaw|