PHENOMINER

Semantic mining of phenotype associations from the biomedical literature

 Coordinatore EUROPEAN MOLECULAR BIOLOGY LABORATORY 

 Organization address address: Meyerhofstrasse 1
city: HEIDELBERG
postcode: 69117

contact info
Titolo: Ms.
Nome: Nishma
Cognome: Chauhan
Email: send email
Telefono: +44 1223 492 524

 Nazionalità Coordinatore Germany [DE]
 Totale costo 278˙807 €
 EC contributo 278˙807 €
 Programma FP7-PEOPLE
Specific programme "People" implementing the Seventh Framework Programme of the European Community for research, technological development and demonstration activities (2007 to 2013)
 Code Call FP7-PEOPLE-2011-IIF
 Funding Scheme MC-IIF
 Anno di inizio 2012
 Periodo (anno-mese-giorno) 2012-11-01   -   2014-12-30

 Partecipanti

# participant  country  role  EC contrib. [€] 
1    EUROPEAN MOLECULAR BIOLOGY LABORATORY

 Organization address address: Meyerhofstrasse 1
city: HEIDELBERG
postcode: 69117

contact info
Titolo: Ms.
Nome: Nishma
Cognome: Chauhan
Email: send email
Telefono: +44 1223 492 524

DE (HEIDELBERG) coordinator 278˙807.40

Mappa


 Word cloud

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

diseases    data    public    literature    solutions    database    community   

 Obiettivo del progetto (Objective)

'Genetic dispositions play an important role in numerous diseases with high relevance to the general public such as Alzheimer's disease, diabetes and multiple sclerosis. Researchers have employed high throughput experiments to discover evidence for the causes and propose novel treatments and cures. This however has led to an explosion in the quantity of primary literature which again needs to be integrated into the discovery process either by increased human efforts or by IT solutions directly delivering the content into a public database. We consider that the phenotype data resources as well as the available text processing solutions are now mature enough to automatically mine the biomedical literature . PHENOMINER's major contribution will be to exploit state-of-the-art text processing solutions with existing ontological resources to collect fragmented biological results about the molecular mechanisms of heritable diseases and to integrate the data into a machine understandable semantic representation for sharing with the community in a public database. The new database will allow results to be immediately available to the bioinformatics community for integration with ongoing laboratory work.'

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