Scipediacontent: Scipediacontent moved page Draft Content 909688648 to Frangi et al 2019a

2021-02-02T07:24:20Z

Scipediacontent moved page Draft Content 909688648 to Frangi et al 2019a

Scipediacontent: Created page with " == Abstract == With the exponential growth of clinical data, and the fast development of AI technologies, researchers are facing unprecedented challenges in managing data st..."

2021-02-02T07:24:17Z

Created page with " == Abstract == With the exponential growth of clinical data, and the fast development of AI technologies, researchers are facing unprecedented challenges in managing data st..."

New page

== Abstract ==

With the exponential growth of clinical data, and the fast development of AI technologies, researchers are facing unprecedented challenges in managing data storage, scalable processing, and analysis capabilities for heterogeneous multisourced datasets. Beyond the complexity of executing data-intensive workflows over large-scale distributed data, the reproducibility of computed results is of paramount importance to validate scientific discoveries. In this paper, we present MULTIX, a cross-domain research-oriented platform, designed for collaborative and reproducible science. This cloud-based framework simplifies the logistical challenges of implementing data analytics and AI solutions by providing pre-configured environments with ad-hoc scalable computing resources and secure distributed storage, to efficiently build, test, share and reproduce scientific pipelines. An exemplary use-case in the area of cardiac image analysis will be presented together with the practical application of the platform for the analysis of ~20.000 subjects of the UK-Biobank database.

== Original document ==

The different versions of the original document can be found in:

* [http://dx.doi.org/10.1109/bibm.2018.8621317 http://dx.doi.org/10.1109/bibm.2018.8621317]

* [http://eprints.whiterose.ac.uk/143253/1/IEEE-BIBM-2018_MULTI-X.pdf http://eprints.whiterose.ac.uk/143253/1/IEEE-BIBM-2018_MULTI-X.pdf]

* [http://xplorestaging.ieee.org/ielx7/8609864/8621069/08621317.pdf?arnumber=8621317 http://xplorestaging.ieee.org/ielx7/8609864/8621069/08621317.pdf?arnumber=8621317],
: [http://dx.doi.org/10.1109/bibm.2018.8621317 http://dx.doi.org/10.1109/bibm.2018.8621317]

* [https://dblp.uni-trier.de/db/conf/bibm/bibm2018.html#VilaAPF18 https://dblp.uni-trier.de/db/conf/bibm/bibm2018.html#VilaAPF18],
: [http://eprints.whiterose.ac.uk/143253 http://eprints.whiterose.ac.uk/143253],
: [https://academic.microsoft.com/#/detail/2911856672 https://academic.microsoft.com/#/detail/2911856672]

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Frangi et al 2019a - Revision history

Scipediacontent: Scipediacontent moved page Draft Content 909688648 to Frangi et al 2019a

Scipediacontent: Created page with " == Abstract == With the exponential growth of clinical data, and the fast development of AI technologies, researchers are facing unprecedented challenges in managing data st..."