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

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Forschungsstelle

INNOSUISSE

Projektnummer

9428.1;4 PFLS-LS

Projekttitel

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

Projekttitel Englisch

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

Texte zu diesem Projekt

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Kurzbeschreibung	-	-	-
Abstract	-	-	-

Erfasste Texte

Kategorie	Text
Kurzbeschreibung (Englisch)	Development of high quality datasets, methods, and tools for large-scale biomolecular data integration
Abstract (Englisch)	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.

Kategorie

Text

Kurzbeschreibung
(Englisch)

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

Abstract
(Englisch)

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.