Enabling Large-Scale Biomedical Analysis in the Cloud

Lin, Yen-Ting; Yu, Chin-Sheng; Lin, Yen-Jen

doi:10.1155/2013/185679

Cited by 17 publications

(9 citation statements)

References 64 publications

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“…With research for more optimal and powerful cloud computing machinery gaining momentum, there emerges a trend that cloud computing creates big data, while big data demand cloud computing in the quest for a sustainable e-infrastructure [Zhang et al 2014a;Lin et al 2013;Choi et al 2013]. More importantly, "data-driven discovery" or synthesis of science, dubbed the "e-Science," is gradually recognized as the "fourth paradigm of science" [Tolle et al 2011], where in history the experimental science based empirical discovery and description of natural phenomena is regarded as the first paradigm, the theoretical science (e.g., Maxwell's equations) the second, and the computer simulation based "virtual" systems or phenomena the third [Kuhn 2012].…”

Section: Scheduling Cloud Resources For Big Data and Cyber-physical Imentioning

confidence: 99%

Cloud Computing Resource Scheduling and a Survey of Its Evolutionary Approaches

et al. 2015

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A disruptive technology fundamentally transforming the way that computing services are delivered, cloud computing offers information and communication technology users a new dimension of convenience of resources, as services via the Internet. Because cloud provides a finite pool of virtualized on-demand resources, optimally scheduling them has become an essential and rewarding topic, where a trend of using Evolutionary Computation (EC) algorithms is emerging rapidly. Through analyzing the cloud computing architecture, this survey first presents taxonomy at two levels of scheduling cloud resources. It then paints a landscape of the scheduling problem and solutions. According to the taxonomy, a comprehensive survey of state-of-the-art approaches is presented systematically. Looking forward, challenges and potential future research directions are investigated and invited, including real-time scheduling, adaptive dynamic scheduling, large-scale scheduling, multiobjective scheduling, and distributed and parallel scheduling. At the dawn of Industry 4.0, cloud computing scheduling for cyber-physical integration with the presence of big data is also discussed. Research in this area is only in its infancy, but with the rapid fusion of information and data technology, more exciting and agenda-setting topics are likely to emerge on the horizon. Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from Permissions@acm.org.

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Section: Scheduling Cloud Resources For Big Data and Cyber-physical Imentioning

confidence: 99%

Cloud Computing Resource Scheduling and a Survey of Its Evolutionary Approaches

et al. 2015

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“…This would give the broadest access, enabling widespread use and collaboration (clinicians, scientists, patients) and, we hope, readying the infrastructure for a future in which genetic data routinely enhance clinical care. This approach has been used at some centers, for example, in association with the Electronic Medical Records and Genomics consortium, 27 but it requires substantial investment to handle the computational burden 28 and some accommodations in terms of information security. With widespread accessibility and given its sensitivity and vastness, one approach is to deidentify the data sets.…”

Section: Adding Genomics To the Ehrmentioning

confidence: 99%

Merging Electronic Health Record Data and Genomics for Cardiovascular Research

Hall¹,

Ryan²,

Bray³

et al. 2016

Circ Cardiovasc Genet

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The process of scientific discovery is rapidly evolving. The funding climate has influenced a favorable shift in scientific discovery toward the use of existing resources such as the electronic health record. The electronic health record enables long-term outlooks on human health and disease, in conjunction with multidimensional phenotypes that include laboratory data, images, vital signs, and other clinical information. Initial work has confirmed the utility of the electronic health record for understanding mechanisms and patterns of variability in disease susceptibility, disease evolution, and drug responses. The addition of biobanks and genomic data to the information contained in the electronic health record has been demonstrated. The purpose of this statement is to discuss the current challenges in and the potential for merging electronic health record data and genomics for cardiovascular research.

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“…Indeed, cloud computing has recently and repeatedly been advocated as a viable infrastructure and economic model to support scalable data-intensive computing, and exemplars of cloud-based data processing architectures for genomics are beginning to emerge. Recent examples include the suite of cloud-based bioinformatics tools described in [8] and the Advanced Sequence Automated Pipeline (ASAP) framework [9]. Both these solutions offer little flexibility for re-coding and experimenting with workflow variations, however.…”

Section: Related Workmentioning

confidence: 99%

From Scripted HPC-Based NGS Pipelines to Workflows on the Cloud

Cała

Wijaya

et al. 2014

2014 14th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing

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Abstract-In this paper we describe our initial experiences in the Cloud-e-Genome project with moving the whole exome sequencing pipeline from the scripted HPC-based solution to a workflow enactment system running in the cloud. We discuss shortcomings of the existing approach based on scripts and list benefits that a workflow-based solution can provide. Despite the effort it involved to wrap all required tools in the form of workflow blocks and the restrictions of the dataflow model used to represent workflows we expect the migration to significantly improve the current status of the pipeline. Our target is to enable flexibility, traceability and reproducibility of the solution, so that it can better fit the evolution of tools, data and pipeline itself and allow us to run it at national scale. This work will become foundation for the more complete system that includes variant filtering and interpretation for the diagnostic purposes.

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Enabling Large-Scale Biomedical Analysis in the Cloud

Cited by 17 publications

References 64 publications

Cloud Computing Resource Scheduling and a Survey of Its Evolutionary Approaches

Cloud Computing Resource Scheduling and a Survey of Its Evolutionary Approaches

Merging Electronic Health Record Data and Genomics for Cardiovascular Research

From Scripted HPC-Based NGS Pipelines to Workflows on the Cloud

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