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PredAlgoBC SIGNED

Machine learning prediction for breast cancer therapy

Total Cost €

0

EC-Contrib. €

0

Partnership

0

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 PredAlgoBC project word cloud

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

pipeline    discovery    tumor    homogeneous    innovative    option    bioinfomics    structure    cells    technologies    predictive    databases    models    complexity    normalization    platform    machine    driver    cancer    regional    populations    mathematics    personalized    association    worldwide    incidence    limited    biology    advocate    analyze    combining    subclonal    search    ico    biomarkers    subtypes    bioinformaticians    geo    clinicians    data    multidisciplinary    marker    grant    center    cross    learning    mainly    reveal    microenvironment    efficient    alterations    entity    treatment    overview    compromised    breast    markers    overcome    methodological    single    death    heterogeneity    biological    observations    sufficient    datasets    stored    throughput    mining    dimension    difficulty    highest    lack    treatments    treat    cell    strategy    therapeutic    genetic    guiding    interface    women    resistance    omics    statistical    mathematicians    patients    immune    tools    stromal    arrayexpress    bulk    types    medicine    optimal    power    algorithms   

Project "PredAlgoBC" data sheet

The following table provides information about the project.

Coordinator
INSTITUT DE CANCEROLOGIE DE L'OUEST 

Organization address
address: 15 RUE ANDRE BOQUEL, CS10059
city: ANGERS
postcode: 49100
website: https://www.centrepaulpapin.org/

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 France [FR]
 Total cost 184˙707 €
 EC max contribution 184˙707 € (100%)
 Programme 1. H2020-EU.1.3.2. (Nurturing excellence by means of cross-border and cross-sector mobility)
 Code Call H2020-MSCA-IF-2018
 Funding Scheme MSCA-IF-EF-ST
 Starting year 2019
 Duration (year-month-day) from 2019-10-01   to  2021-09-30

 Partnership

Take a look of project's partnership.

# participants  country  role  EC contrib. [€] 
1    INSTITUT DE CANCEROLOGIE DE L'OUEST FR (ANGERS) coordinator 184˙707.00

Map

 Project objective

Breast cancer is the cancer with the highest incidence in women worldwide, and is the leading cause of cancer-related death, mainly due to treatment resistance. Recently, tumor heterogeneity has been described as one of the key driver in treatment failure. Indeed, tumor is not a homogeneous entity to treat, but a complex association of subclonal populations driven by their own genetic alterations, and immune and stromal cells from microenvironment. Breast cancer subtypes and tumor heterogeneity advocate for the development of tailored, personalized treatments, but so far, the discovery of efficient predictive markers has been compromised by the lack of adapted biological models and methodological tools. The recent developments of high-throughput methods for bulk and single-cell analyses has generated large ‘omics’ datasets from patients, stored in open access databases (ArrayExpress, GEO). Combining these numerous datasets will grant a sufficient statistical power to reveal a comprehensive overview of tumor complexity. However, this data mining is currently limited by methodological challenges like cross-platform normalization and the difficulty to analyze complex data structure with high dimension observations. To overcome these issues, I propose to implement a multidisciplinary project at the interface between mathematics, biology, and information technologies. With the support of the mathematicians and bioinformaticians from the Bioinfomics unit of the regional comprehensive cancer center (ICO), I will develop and implement machine-learning algorithms in the search of predictive biomarkers for breast cancer treatment. This innovative strategy will lead to personalized medicine in breast cancer by guiding clinicians in the selection of the optimal therapeutic option. Moreover, this generated pipeline for predictive marker discovery could be further adapted for the treatment of other cancer types.

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The information about "PREDALGOBC" are provided by the European Opendata Portal: CORDIS opendata.

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