Explore the words cloud of the iPC project. It provides you a very rough idea of what is the project "iPC" about.
The following table provides information about the project.
Coordinator |
TECHNIKON FORSCHUNGS- UND PLANUNGSGESELLSCHAFT MBH
Organization address contact info |
Coordinator Country | Austria [AT] |
Total cost | 15˙066˙525 € |
EC max contribution | 14˙748˙400 € (98%) |
Programme |
1. H2020-EU.3.1.5.3. (Using in-silico medicine for improving disease management and prediction) |
Code Call | H2020-SC1-DTH-2018-1 |
Funding Scheme | RIA |
Starting year | 2019 |
Duration (year-month-day) | from 2019-01-01 to 2022-12-31 |
Take a look of project's partnership.
Effective personalized medicine for paediatric cancers must address a multitude of challenges, including domain-specific challenges. To overcome these challenges, we propose a comprehensive computational effort to combine knowledge-base, machine-learning, and mechanistic models to predict optimal standard and experimental therapies for each child. Our approach is based on virtual patient models–in-silico avatars whose analysis can inform personalized diagnostics and recommend treatments. Our platform will also allow care givers to query models and infer benefits and drawbacks for specific treatment combinations for each child. To construct these models, we will combine state-of-the-art computational methods and data from molecular assays, and clinical and preclinical studies. We will test their predictions prospectively on data from clinical trials and test therapies in pre-clinical settings. We will focus on a select panel of paediatric tumours including both high-incidence and high-risk tumour types. To accomplish our goals, we have assembled an interdisciplinary team consisting of basic, translational, and clinical researchers—all amongst the leaders in their respective fields—and established strong relationships with European Centres of Excellence, patient organizations, and clinical trials focus on personalized medicine for our proposed case studies. We will produce, assemble, standardize, and harmonize accessible high-quality multi-disciplinary data and leverage the potential of Big Data and HPC for the personalized treatments of European citizens. We will make our models and data available through a cloud-based platform, whose exploitation will be maximised through a collaboration with the European Open Science Cloud initiative. In summary, iPC will address the critical need for personalized medicine for children with cancer, contribute to the digitalization of clinical workflows, and enable the Digital Single Market of the EU data infrastructure.
year | authors and title | journal | last update |
---|---|---|---|
2019 |
Dominik Linzner
Michael Schmidt
Heinz Koeppl Scalable Structure Learning of Continuous-Time Bayesian Networks from Incomplete Data published pages: , ISSN: , DOI: |
33rd Conference on Neural Information Processing Systems (NeurIPS 2019) | 2020-03-23 |
2019 |
Dominik Linzner
Heinz Koeppl A Variational Perturbative Approach to Planning in Graph-based Markov Decision Processes published pages: , ISSN: , DOI: |
Association for the Advancement of Artificial Intelligence | 2020-03-23 |
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The information about "IPC" are provided by the European Opendata Portal: CORDIS opendata.