Name
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 multi-sourced 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 MULTI-X, 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 similar to 20.000 subjects of the UK-Biobank database. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/BIBM.2018.8621317 | PROCEEDINGS 2018 IEEE INTERNATIONAL CONFERENCE ON BIOINFORMATICS AND BIOMEDICINE (BIBM) |
Keywords | Field | DocType |
biomedical informatics, precision medicine, cloud computing, population analysis | Data science,Data analysis,Computer science,Computer data storage,Distributed data store,Artificial intelligence,Health informatics,Workflow,Machine learning,Scalable computing,Cloud computing,Scalability | Conference |
ISSN | Citations | PageRank |
2156-1125 | 0 | 0.34 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Milton Hoz de Vila | 1 | 0 | 0.34 |
| Rahman Attar | 2 | 4 | 3.04 |
| Marco Pereañez | 3 | 31 | 6.77 |
| Alejandro F. Frangi | 4 | 4333 | 309.21 |