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Circ Res. 2018 Apr 27;122(9):1290-1301. doi: 10.1161/CIRCRESAHA.117.310967.

Biomedical Informatics on the Cloud: A Treasure Hunt for Advancing Cardiovascular Medicine.

Ping P1,2,3,4, Hermjakob H5,6, Polson JS5,2, Benos PV7,8, Wang W5,4.

Author information

1
From the NIH BD2K Center of Excellence for Biomedical Computing at UCLA (HeartBD2K), Los Angeles, CA (P.P., H.H., J.S.P., W.W.) ppingucla@gmail.com.
2
Department of Physiology (P.P., J.S.P.).
3
Department of Medicine (P.P.).
4
UCLA School of Medicine, Los Angeles, CA; Department of Computer Science, Scalable Analytics Institute, UCLA School of Engineering, Los Angeles, CA (P.P., W.W.).
5
From the NIH BD2K Center of Excellence for Biomedical Computing at UCLA (HeartBD2K), Los Angeles, CA (P.P., H.H., J.S.P., W.W.).
6
Molecular Systems Cluster, European Molecular Biology Laboratory-European Bioinformatics Institute (EMBL-EBI), Cambridge, United Kingdom (H.H.).
7
Departments of Computational & Systems Biology, School of Medicine, University of Pittsburgh, PA (P.V.B.).
8
NIH BD2K Center of Excellence for Biomedical Computing at University of Pittsburgh (Center for Causal Discovery), PA (P.V.B.).

Abstract

In the digital age of cardiovascular medicine, the rate of biomedical discovery can be greatly accelerated by the guidance and resources required to unearth potential collections of knowledge. A unified computational platform leverages metadata to not only provide direction but also empower researchers to mine a wealth of biomedical information and forge novel mechanistic insights. This review takes the opportunity to present an overview of the cloud-based computational environment, including the functional roles of metadata, the architecture schema of indexing and search, and the practical scenarios of machine learning-supported molecular signature extraction. By introducing several established resources and state-of-the-art workflows, we share with our readers a broadly defined informatics framework to phenotype cardiovascular health and disease.

KEYWORDS:

cardiovascular disease; environment; informatics; machine learning; metadata

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