TY - GEN
T1 - MULTI-X, a State-of-the-Art Cloud-Based Ecosystem for Biomedical Research
AU - De Vila, Milton Hoz
AU - Attar, Rahman
AU - Pereanez, Marco
AU - Frangi, Alejandro F.
N1 - Funding Information:
VIII. ACKNOWLEDGMENT This work was supported in part by the European Research Council (Back-UP, ID 777090). Our thanks to Amazon Company for funding this research through AWS Research Grant (2018-2019).
Publisher Copyright:
© 2018 IEEE.
PY - 2019/1/21
Y1 - 2019/1/21
N2 - 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.
AB - 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.
KW - biomedical informatics
KW - cloud computing
KW - population analysis
KW - precision medicine
UR - http://www.scopus.com/inward/record.url?scp=85062523611&partnerID=8YFLogxK
U2 - 10.1109/BIBM.2018.8621317
DO - 10.1109/BIBM.2018.8621317
M3 - Conference contribution
AN - SCOPUS:85062523611
T3 - Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018
SP - 1726
EP - 1733
BT - Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018
A2 - Schmidt, Harald
A2 - Griol, David
A2 - Wang, Haiying
A2 - Baumbach, Jan
A2 - Zheng, Huiru
A2 - Callejas, Zoraida
A2 - Hu, Xiaohua
A2 - Dickerson, Julie
A2 - Zhang, Le
PB - IEEE
T2 - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018
Y2 - 3 December 2018 through 6 December 2018
ER -