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A collaborative approach to develop a multi-omics data analytics platform for translational research

The integration and analysis of large datasets in translational research has become an increasingly challenging problem. We propose a collaborative approach to integrate established data management platforms with existing analytical systems to fill the hole in the value chain between data collection...

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Detalles Bibliográficos
Autores principales: Schumacher, Axel, Rujan, Tamas, Hoefkens, Jens
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2014
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4888831/
https://www.ncbi.nlm.nih.gov/pubmed/27294023
http://dx.doi.org/10.1016/j.atg.2014.09.010
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author Schumacher, Axel
Rujan, Tamas
Hoefkens, Jens
author_facet Schumacher, Axel
Rujan, Tamas
Hoefkens, Jens
author_sort Schumacher, Axel
collection PubMed
description The integration and analysis of large datasets in translational research has become an increasingly challenging problem. We propose a collaborative approach to integrate established data management platforms with existing analytical systems to fill the hole in the value chain between data collection and data exploitation. Our proposal in particular ensures data security and provides support for widely distributed teams of researchers. As a successful example for such an approach, we describe the implementation of a unified single platform that combines capabilities of the knowledge management platform tranSMART and the data analysis system Genedata Analyst™. The combined end-to-end platform helps to quickly find, enter, integrate, analyze, extract, and share patient- and drug-related data in the context of translational R&D projects.
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spelling pubmed-48888312016-06-10 A collaborative approach to develop a multi-omics data analytics platform for translational research Schumacher, Axel Rujan, Tamas Hoefkens, Jens Appl Transl Genom Special Issue - Genomic Knowledge Sharing The integration and analysis of large datasets in translational research has become an increasingly challenging problem. We propose a collaborative approach to integrate established data management platforms with existing analytical systems to fill the hole in the value chain between data collection and data exploitation. Our proposal in particular ensures data security and provides support for widely distributed teams of researchers. As a successful example for such an approach, we describe the implementation of a unified single platform that combines capabilities of the knowledge management platform tranSMART and the data analysis system Genedata Analyst™. The combined end-to-end platform helps to quickly find, enter, integrate, analyze, extract, and share patient- and drug-related data in the context of translational R&D projects. Elsevier 2014-09-16 /pmc/articles/PMC4888831/ /pubmed/27294023 http://dx.doi.org/10.1016/j.atg.2014.09.010 Text en © 2014 Published by Elsevier B.V. http://creativecommons.org/licenses/by-nc-nd/3.0/ This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/3.0/).
spellingShingle Special Issue - Genomic Knowledge Sharing
Schumacher, Axel
Rujan, Tamas
Hoefkens, Jens
A collaborative approach to develop a multi-omics data analytics platform for translational research
title A collaborative approach to develop a multi-omics data analytics platform for translational research
title_full A collaborative approach to develop a multi-omics data analytics platform for translational research
title_fullStr A collaborative approach to develop a multi-omics data analytics platform for translational research
title_full_unstemmed A collaborative approach to develop a multi-omics data analytics platform for translational research
title_short A collaborative approach to develop a multi-omics data analytics platform for translational research
title_sort collaborative approach to develop a multi-omics data analytics platform for translational research
topic Special Issue - Genomic Knowledge Sharing
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4888831/
https://www.ncbi.nlm.nih.gov/pubmed/27294023
http://dx.doi.org/10.1016/j.atg.2014.09.010
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