Integrating multiple types of data for signaling research: challenges and opportunities

New technologies promise to provide unprecedented amounts of information that can build a foundation for creating predictive models of cell signaling pathways. To be useful, however, this information must be integrated into a coherent framework. In addition, the sheer volume of data gathered from th...

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Bibliographic Details
Published in:Science signaling 2011-02, Vol.4 (160), p.pe9-pe9
Main Author: Wiley, H Steven
Format: Article
Language:English
Subjects:
60 APPLIED LIFE SCIENCES
BASIC BIOLOGICAL SCIENCES
bioinformatics
BIOLOGICAL FUNCTIONS
CELL CONSTITUENTS
Computational Biology - methods
DATA ANALYSIS
Database Management Systems - standards
genomics
Genomics - standards
Genomics - statistics & numerical data
Humans
integration
proteomics
Proteomics - standards
Proteomics - statistics & numerical data
Research Design
Signal Transduction
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Summary:New technologies promise to provide unprecedented amounts of information that can build a foundation for creating predictive models of cell signaling pathways. To be useful, however, this information must be integrated into a coherent framework. In addition, the sheer volume of data gathered from the new technologies requires computational approaches for its analysis. Unfortunately, there are many barriers to data integration and analysis, mostly because of a lack of adequate data standards and their inconsistent use by scientists. However, solving the fundamental issues of data sharing will enable the investigation of entirely new areas of cell signaling research.
ISSN:1945-0877
1937-9145
DOI:10.1126/scisignal.2001826