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iPC SIGNED

individualizedPaediatricCure: Cloud-based virtual-patient models for precision paediatric oncology

Total Cost €

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EC-Contrib. €

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Partnership

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 iPC project word cloud

Explore the words cloud of the iPC project. It provides you a very rough idea of what is the project "iPC" about.

consisting    maximised    disciplinary    models    platform    cloud    standardize    optimal    goals    assembled    diagnostics    panel    trials    care    child    therapies    benefits    combine    cancer    query    domain    excellence    hpc    computational    incidence    treatments    data    learning    settings    single    team    drawbacks    avatars    base    effort    assemble    assays    predictions    machine    paediatric    select    combinations    workflows    digitalization    leaders    types    infer    translational    clinical    experimental    silico    critical    givers    inform    amongst    basic    virtual    personalized    market    big    digital    relationships    preclinical    accessible    centres    harmonize    medicine    tumours    science    mechanistic    overcome    prospectively    ipc    cancers    infrastructure    interdisciplinary    construct    molecular    standard    quality    recommend    summary    risk    effective    patient    multitude    predict    citizens    leverage    treatment    respective    tumour    children   

Project "iPC" data sheet

The following table provides information about the project.

Coordinator
TECHNIKON FORSCHUNGS- UND PLANUNGSGESELLSCHAFT MBH 

Organization address
address: BURGPLATZ 3A
city: VILLACH
postcode: 9500
website: https://urldefense.com/v3/__https://technikon.com__;!!DOxrgLBm!XQj8lX3wvrhxnrAflv0OBz99-qlU79olpwsKnLk7T4NxnEuLngVJcmEmxcnyX1OKvqUsEW_8DocHehV2MbBX$

contact info
title: n.a.
name: n.a.
surname: n.a.
function: n.a.
email: n.a.
telephone: n.a.
fax: n.a.

 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

 Partnership

Take a look of project's partnership.

# participants  country  role  EC contrib. [€] 
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

Map

 Project objective

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.

 Publications

year authors and title journal last update
List of publications.
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.

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