Reference : Fractalis: A scalable open-source service for platform-independent interactive visual...
Scientific journals : Article
Engineering, computing & technology : Computer science
Computational Sciences
http://hdl.handle.net/10993/36438
Fractalis: A scalable open-source service for platform-independent interactive visual analysis of biomedical data
English
Herzinger, Sascha mailto [University of Luxembourg > Luxembourg Centre for Systems Biomedicine (LCSB) >]
Groues, Valentin mailto [University of Luxembourg > Luxembourg Centre for Systems Biomedicine (LCSB) >]
Gu, Wei mailto [University of Luxembourg > Luxembourg Centre for Systems Biomedicine (LCSB) >]
Satagopam, Venkata mailto [University of Luxembourg > Luxembourg Centre for Systems Biomedicine (LCSB) >]
Banda, Peter mailto [University of Luxembourg > Luxembourg Centre for Systems Biomedicine (LCSB) >]
Trefois, Christophe mailto [University of Luxembourg > Luxembourg Centre for Systems Biomedicine (LCSB) >]
Schneider, Reinhard mailto [University of Luxembourg > Luxembourg Centre for Systems Biomedicine (LCSB) >]
27-Aug-2018
GigaScience
Yes
International
[en] web service ; visual analytics ; explorative analysis
[en] Background: Translational research platforms share the aim to promote a deeper understanding of stored data by providing visualization and analysis tools for data exploration and hypothesis generation. However, such tools are usually platform-bound and are not easily reusable by other systems. Furthermore, they rarely address access restriction issues when direct data transfer is not permitted. In this article we present an analytical service that works in tandem with a visualization library to address these problems.
Findings: Using a combination of existing technologies and a platform-specific data abstraction layer we developed a service that is capable of providing existing web-based data warehouses and repositories with platform-independent visual analytical capabilities. The design of this service also allows for federated data analysis by eliminating the need to move the data directly to the researcher. Instead, all operations are based on statistics and interactive charts without direct access to the dataset.
Conclusion: The software presented in this article has a potential to help translational researchers achieve a better understanding of a given dataset and quickly generate new hypothesis. Furthermore, it provides a framework that can be used to share and reuse explorative analysis tools within the community.
Luxembourg Centre for Systems Biomedicine (LCSB): Bioinformatics Core (R. Schneider Group)
Researchers ; Professionals
http://hdl.handle.net/10993/36438
10.1093/gigascience/giy109
http://dx.doi.org/10.1093/gigascience/giy109

File(s) associated to this reference

Fulltext file(s):

FileCommentaryVersionSizeAccess
Limited access
Manuscript.docxAuthor preprint1.06 MBRequest a copy

Additional material(s):

File Commentary Size Access
Limited access
Supplementary Material.docx93.48 kBRequest a copy

Bookmark and Share SFX Query

All documents in ORBilu are protected by a user license.