'Big data', Hadoop and cloud computing in genomics
TLDR
An overview of cloud computing and big data technologies, and how such expertise can be used to deal with biology's big data sets is discussed, together with an overview of the current usage of Hadoop within the bioinformatics community.About:
This article is published in Journal of Biomedical Informatics.The article was published on 2013-10-01 and is currently open access. It has received 403 citations till now. The article focuses on the topics: Data-intensive computing & Big data.read more
Citations
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The rise of big data on cloud computing
Ibrahim Abaker Targio Hashem,Ibrar Yaqoob,Nor Badrul Anuar,Salimah Binti Mokhtar,Abdullah Gani,Samee U. Khan +5 more
TL;DR: The definition, characteristics, and classification of big data along with some discussions on cloud computing are introduced, and research challenges are investigated, with focus on scalability, availability, data integrity, data transformation, data quality, data heterogeneity, privacy, legal and regulatory issues, and governance.
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MeDShare: Trust-Less Medical Data Sharing Among Cloud Service Providers via Blockchain
TL;DR: The proposed MeDShare system is blockchain-based and provides data provenance, auditing, and control for shared medical data in cloud repositories among big data entities and employs smart contracts and an access control mechanism to effectively track the behavior of the data.
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Perspective: Materials informatics and big data: Realization of the “fourth paradigm” of science in materials science
Ankit Agrawal,Alok Choudhary +1 more
TL;DR: In this article, the authors look at how data-driven techniques are playing a big role in deciphering processing-structure-property-performance relationships in materials, with illustrative examples of both forward models (property prediction) and inverse models (materials discovery).
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Industry 4.0 and Health: Internet of Things, Big Data, and Cloud Computing for Healthcare 4.0
TL;DR: By selectively analyzing the literature, this paper systematically survey how the adoption of the above-mentioned Industry 4.0 technologies (and their integration) applied to the health domain is changing the way to provide traditional services and products.
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Big Data: Survey, Technologies, Opportunities, and Challenges
Nawsher Khan,Ibrar Yaqoob,Ibrahim Abaker Targio Hashem,Zakira Inayat,Waleed Kamaleldin Mahmoud Ali,Muhammad Alam,Muhammad Shiraz,Abdullah Gani +7 more
TL;DR: This study comprehensively surveys and classifies the various attributes of Big data, including its nature, definitions, rapid growth rate, volume, management, analysis, and security, and proposes a data life cycle that uses the technologies and terminologies of Big Data.
References
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Gene set analysis in the cloud
TL;DR: A gene set analysis algorithm for biomarker identification in the cloud, YunBe, is developed and ready to use on Amazon Web Services and here it is compared to those obtained with desktop and computing cluster solutions.
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Genotyping in the Cloud with Crossbow
TL;DR: This unit will demonstrate the use of Crossbow for identifying variations in three different operating modes: on a Hadoop cluster, on a single computer, and on the Amazon Elastic MapReduce cloud computing service.
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A de novo next generation genomic sequence assembler based on string graph and MapReduce cloud computing framework.
TL;DR: A distributed genome assembler based on string graphs and MapReduce framework, known as the CloudBrush, which includes a novel edge-adjustment algorithm to detect structural defects by examining the neighboring reads of a specific read for sequencing errors and adjusting the edges of the string graph, if necessary.
Journal Article
Cloudy Confidentiality: Clinical and Legal Implications of Cloud Computing in Health Care
TL;DR: This article is a review of the legal background of protected health records, as well as cloud technology and physician applications, and the legal regulations that may inform care and standards of practice are reviewed.
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Protein folding by distributed computing and the denatured state ensemble
TL;DR: This study uses a coarse-grain model of protein L, whose two-state kinetics have been characterized in detail by using long-time equilibrium simulations, to rigorously test a FH protocol and shows that the FH results give non-Poisson distributions and early folding events that are unphysical, whereas longer folding events experience a correct barrier to folding but are not representative of the equilibrium folding ensemble.