SMAC

Statistical machine learning for complex biological data

Coordinatore	ASSOCIATION POUR LA RECHERCHE ET LE DEVELOPPEMENT DES METHODES ET PROCESSUS INDUSTRIELS - ARMINES    more Spiacenti, non ci sono informazioni su questo coordinatore. Contattare Fabio per maggiori infomrazioni, grazie.
Nazionalità Coordinatore	France [FR]
Totale costo	1˙496˙004 €
EC contributo	1˙496˙004 €
Programma	FP7-IDEAS-ERC Specific programme: "Ideas" implementing the Seventh Framework Programme of the European Community for research, technological development and demonstration activities (2007 to 2013)
Code Call	ERC-2011-StG_20101014
Funding Scheme	ERC-SG
Anno di inizio	2012
Periodo (anno-mese-giorno)	2012-02-01   -   2017-01-31

 Partecipanti

#	participant	country	role	EC contrib. [€]
1	ASSOCIATION POUR LA RECHERCHE ET LE DEVELOPPEMENT DES METHODES ET PROCESSUS INDUSTRIELS - ARMINES  Organization address address: Boulevard Saint-Michel 60 city: PARIS postcode: 75272 contact info Titolo: Mr. Nome: Jean-Philippe Cognome: Vert Email: send email Telefono: +33 1 64 69 47 82 Fax: +33 1 64 69 47 05	FR (PARIS)	hostInstitution	1˙496˙004.00
2	ASSOCIATION POUR LA RECHERCHE ET LE DEVELOPPEMENT DES METHODES ET PROCESSUS INDUSTRIELS - ARMINES  Organization address address: Boulevard Saint-Michel 60 city: PARIS postcode: 75272 contact info Titolo: Ms. Nome: Sophie Cognome: Cousin Email: send email Telefono: +33 1 40 51 93 77 Fax: +33 1 46 34 23 05	FR (PARIS)	hostInstitution	1˙496˙004.00

Mappa

 Word cloud

Esplora la "nuvola delle parole (Word Cloud) per avere un'idea di massima del progetto.

 Obiettivo del progetto (Objective)

'This interdisciplinary project aims to develop new statistical and machine learning approaches to analyze high-dimensional, structured and heterogeneous biological data. We focus on the cases where a relatively small number of samples are characterized by huge quantities of quantitative features, a common situation in large-scale genomic projects, but particularly challenging for statistical inference. In order to overcome the curse of dimension we propose to exploit the particular structures of the data, and encode prior biological knowledge in a unified, mathematically sound, and computationally efficient framework. These methodological development, both theoretical and practical, will be guided by and applied to the inference of predictive models and the detection of predictive factors for prognosis and drug response prediction in cancer.'