Development of high quality datasets, methods, and tools for large-scale biomolecular data integration

Fonte Standard Gras
Identifiant

Unité de recherche

INNOSUISSE

Numéro de projet

9428.1;4 PFLS-LS

Titre du projet

Development of high quality datasets, methods, and tools for large-scale biomolecular data integration

Titre du projet anglais

Development of high quality datasets, methods, and tools for large-scale biomolecular data integration

Textes relatifs à ce projet

	Allemand	Français	Italien	Anglais
Description succincte	-	-	-
Résumé des résultats (Abstract)	-	-	-

Textes saisis

Catégorie	Texte
Description succincte (Anglais)	Development of high quality datasets, methods, and tools for large-scale biomolecular data integration
Résumé des résultats (Abstract) (Anglais)	The increasing availability of public high-throughput biomolecular data opens new perspectives in biologicaldiscovery, drug development, and diagnostics. Integrating this massive amount of data in its various forms(gene, transcript, protein, metabolite) poses a major bioinformatical challenge. This project aims ataccomplishing three main challenges: first, several manually curated high quality datasets from multiple datatypes will be estalished. Second, because the data is very heterogenous and voluminous, new methods willbe developed to process them into a systematic format that facilitates their integration. Third, novel toolsand high-performance algorithms will be developed to facilitate the meta-analysis of multiple data types.

Catégorie

Texte

Description succincte
(Anglais)

Development of high quality datasets, methods, and tools for large-scale biomolecular data integration

Résumé des résultats (Abstract)
(Anglais)

The increasing availability of public high-throughput biomolecular data opens new perspectives in biologicaldiscovery, drug development, and diagnostics. Integrating this massive amount of data in its various forms(gene, transcript, protein, metabolite) poses a major bioinformatical challenge. This project aims ataccomplishing three main challenges: first, several manually curated high quality datasets from multiple datatypes will be estalished. Second, because the data is very heterogenous and voluminous, new methods willbe developed to process them into a systematic format that facilitates their integration. Third, novel toolsand high-performance algorithms will be developed to facilitate the meta-analysis of multiple data types.