The perfect neuroimaging-genetics-computation storm: collision of petabytes of data, millions of hardware devices and thousands of software tools
DC Field | Value | Language |
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dc.contributor.author | Dinov, Ivo D. | - |
dc.contributor.author | Petrosyan, Petros | - |
dc.contributor.author | Liu, Zhizhong | - |
dc.contributor.author | Eggert, Paul | - |
dc.contributor.author | Zamanyan, Alen | - |
dc.contributor.author | Torri, Federica | - |
dc.contributor.author | Macciardi, Fabio | - |
dc.contributor.author | Hobel, Sam | - |
dc.contributor.author | Moon, Seok Woo | - |
dc.contributor.author | Sung, Young Hee | - |
dc.contributor.author | Jiang, Zhiguo | - |
dc.contributor.author | Labus, Jennifer | - |
dc.contributor.author | Kurth, Florian | - |
dc.contributor.author | Ashe-McNalley, Cody | - |
dc.contributor.author | Mayer, Emeran | - |
dc.contributor.author | Vespa, Paul M. | - |
dc.contributor.author | Van Horn, John D. | - |
dc.contributor.author | Toga, Arthur W. | - |
dc.date.available | 2020-02-28T17:42:28Z | - |
dc.date.created | 2020-02-06 | - |
dc.date.issued | 2014-06 | - |
dc.identifier.issn | 1931-7557 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/gachon/handle/2020.sw.gachon/12583 | - |
dc.description.abstract | The volume, diversity and velocity of biomedical data are exponentially increasing providing petabytes of new neuroimaging and genetics data every year. At the same time, tens-of-thousands of computational algorithms are developed and reported in the literature along with thousands of software tools and services. Users demand intuitive, quick and platform-agnostic access to data, software tools, and infrastructure from millions of hardware devices. This explosion of information, scientific techniques, computational models, and technological advances leads to enormous challenges in data analysis, evidence-based biomedical inference and reproducibility of findings. The Pipeline workflow environment provides a crowd-based distributed solution for consistent management of these heterogeneous resources. The Pipeline allows multiple (local) clients and (remote) servers to connect, exchange protocols, control the execution, monitor the states of different tools or hardware, and share complete protocols as portable XML workflows. In this paper, we demonstrate several advanced computational neuroimaging and genetics case-studies, and end-to-end pipeline solutions. These are implemented as graphical workflow protocols in the context of analyzing imaging (sMRI, fMRI, DTI), phenotypic (demographic, clinical), and genetic (SNP) data. | - |
dc.language | 영어 | - |
dc.language.iso | en | - |
dc.publisher | SPRINGER | - |
dc.relation.isPartOf | BRAIN IMAGING AND BEHAVIOR | - |
dc.subject | WHOLE-GENOME ASSOCIATION | - |
dc.subject | COPY NUMBER VARIATION | - |
dc.subject | READ ALIGNMENT | - |
dc.subject | WIDE ASSOCIATION | - |
dc.subject | BIG DATA | - |
dc.subject | VISUALIZATION | - |
dc.subject | ALGORITHMS | - |
dc.subject | ACCURATE | - |
dc.subject | REGISTRATION | - |
dc.subject | INFORMATICS | - |
dc.title | The perfect neuroimaging-genetics-computation storm: collision of petabytes of data, millions of hardware devices and thousands of software tools | - |
dc.type | Article | - |
dc.type.rims | ART | - |
dc.description.journalClass | 1 | - |
dc.identifier.wosid | 000335765700014 | - |
dc.identifier.doi | 10.1007/s11682-013-9248-x | - |
dc.identifier.bibliographicCitation | BRAIN IMAGING AND BEHAVIOR, v.8, no.2, pp.311 - 322 | - |
dc.identifier.scopusid | 2-s2.0-84899952512 | - |
dc.citation.endPage | 322 | - |
dc.citation.startPage | 311 | - |
dc.citation.title | BRAIN IMAGING AND BEHAVIOR | - |
dc.citation.volume | 8 | - |
dc.citation.number | 2 | - |
dc.contributor.affiliatedAuthor | Sung, Young Hee | - |
dc.type.docType | Article | - |
dc.subject.keywordAuthor | Aging | - |
dc.subject.keywordAuthor | Pipeline | - |
dc.subject.keywordAuthor | Neuroimaging | - |
dc.subject.keywordAuthor | Genetics | - |
dc.subject.keywordAuthor | Computation solutions | - |
dc.subject.keywordAuthor | Workflows | - |
dc.subject.keywordAuthor | IBS | - |
dc.subject.keywordAuthor | Pain | - |
dc.subject.keywordAuthor | Parkinson&apos | - |
dc.subject.keywordAuthor | s disease | - |
dc.subject.keywordAuthor | Alzheimer&apos | - |
dc.subject.keywordAuthor | s disease | - |
dc.subject.keywordAuthor | Shape | - |
dc.subject.keywordAuthor | Volume | - |
dc.subject.keywordAuthor | Analysis | - |
dc.subject.keywordAuthor | Big data | - |
dc.subject.keywordAuthor | Visualization | - |
dc.subject.keywordPlus | WHOLE-GENOME ASSOCIATION | - |
dc.subject.keywordPlus | COPY NUMBER VARIATION | - |
dc.subject.keywordPlus | READ ALIGNMENT | - |
dc.subject.keywordPlus | WIDE ASSOCIATION | - |
dc.subject.keywordPlus | BIG DATA | - |
dc.subject.keywordPlus | VISUALIZATION | - |
dc.subject.keywordPlus | ALGORITHMS | - |
dc.subject.keywordPlus | ACCURATE | - |
dc.subject.keywordPlus | REGISTRATION | - |
dc.subject.keywordPlus | INFORMATICS | - |
dc.relation.journalResearchArea | Neurosciences & Neurology | - |
dc.relation.journalWebOfScienceCategory | Neuroimaging | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
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