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.
TECHNIKON FORSCHUNGS- UND PLANUNGSGESELLSCHAFT MBH
|Coordinator Country||Austria [AT]|
|Total cost||15˙066˙525 €|
|EC max contribution||14˙748˙400 € (98%)|
1. H2020-EU.18.104.22.168. (Using in-silico medicine for improving disease management and prediction)
|Duration (year-month-day)||from 2019-01-01 to 2022-12-31|
Take a look of project's partnership.
|1||TECHNIKON FORSCHUNGS- UND PLANUNGSGESELLSCHAFT MBH||AT (VILLACH)||coordinator||513˙004.00|
|2||IBM RESEARCH GMBH||CH (RUESCHLIKON)||participant||2˙020˙506.00|
|3||UNIVERSITAT ZURICH||CH (ZURICH)||participant||1˙451˙213.00|
|4||INSTITUT CURIE||FR (PARIS)||participant||1˙381˙550.00|
|5||BAYLOR COLLEGE OF MEDICINE||US (HOUSTON TX)||participant||1˙104˙991.00|
|6||BARCELONA SUPERCOMPUTING CENTER - CENTRO NACIONAL DE SUPERCOMPUTACION||ES (BARCELONA)||participant||980˙414.00|
|7||ALACRIS THERANOSTICS GMBH||DE (BERLIN)||participant||861˙444.00|
|8||MAX-PLANCK-GESELLSCHAFT ZUR FORDERUNG DER WISSENSCHAFTEN EV||DE (MUENCHEN)||participant||769˙881.00|
|9||TECHNISCHE UNIVERSITAT DARMSTADT||DE (DARMSTADT)||participant||720˙673.00|
|10||UNIVERSITEIT GENT||BE (GENT)||participant||668˙974.00|
|11||PRINSES MAXIMA CENTRUM VOOR KINDERONCOLOGIE BV||NL (UTRECHT)||participant||629˙109.00|
|12||DEUTSCHES KREBSFORSCHUNGSZENTRUM HEIDELBERG||DE (HEIDELBERG)||participant||531˙691.00|
|13||UNIVERSITA DEGLI STUDI DI NAPOLI FEDERICO II||IT (NAPOLI)||participant||482˙857.00|
|14||UNIVERSITATSKLINIKUM HEIDELBERG||DE (HEIDELBERG)||participant||452˙750.00|
|15||ACADEMISCH MEDISCH CENTRUM BIJ DE UNIVERSITEIT VAN AMSTERDAM||NL (AMSTERDAM)||participant||437˙262.00|
|16||LUDWIG-MAXIMILIANS-UNIVERSITAET MUENCHEN||DE (MUENCHEN)||participant||414˙838.00|
|17||INSTITUT DE INVESTIGACIO EN CIENCIES DE LA SALUT GERMANS TRIAS I PUJOL||ES (BADALONA BARCELONA)||participant||397˙398.00|
|18||CONSIGLIO NAZIONALE DELLE RICERCHE||IT (ROMA)||participant||384˙940.00|
|19||XLAB RAZVOJ PROGRAMSKE OPREME IN SVETOVANJE DOO||SI (LJUBLJANA)||participant||369˙243.00|
|20||THE CHILDREN'S HOSPITAL OF PHILADELPHIA NON PROFIT ORG||US (Philadelphia)||participant||175˙652.00|
|21||CHILDREN'S MEDICAL RESEARCH INSTITUTE||AU (WESTMEAD, NEW SOUTH WALES)||participant||0.00|
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|
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|
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.
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