iCircos: Visual Analytics for Translational Bioinformatics
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2012/01/28
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Description:Translational bioinformatics increasingly involves the discovery of associations between molecular and phenotype information, with the goal of transforming those discoveries into novel methods for diagnosis and treatment. To enable such complex analysis, researchers need approaches that provide the simultaneous representation and interactive analysis of patients, and their molecular and phenotype information. Because few existing visual analytical systems provide appropriate capabilities, we developed a prototypical visual analytical system called iCircos, which enables the simultaneous and interactive exploration of molecular and phenotype information. We discuss our overall method for developing the prototype by integrating user needs and design heuristics from visual analytics, with agile programming in HTML5 and SVG. A demonstration of the prototype to explore molecular and phenotype associations in two disease datasets suggests that iCircos has the potential to accelerate translational discoveries in complex disease datasets. We conclude by discussing insights about designing visual analytical systems for translational bioinformatics, and present our future plans for user testing and adding advanced interactivity to the prototype. [Description provided by NIOSH]
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ISBN:9781450307819
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Pages in Document:1-5
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NIOSHTIC Number:nn:20055952
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Citation:IHI 2012: Proceedings of the 2nd ACM SIGHIT International Health Informatics Symposium, January 28-30, 2012, Miami, Florida. New York: Association for Computing Machinery 2012 Jan; :1-5
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Federal Fiscal Year:2012
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Performing Organization:University of Texas Medical Branch, Galveston
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Peer Reviewed:False
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Start Date:20100901
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Source Full Name:IHI 2012: Proceedings of the 2nd ACM SIGHIT International Health Informatics Symposium, January 28-30, 2012, Miami, Florida
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End Date:20130131
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Main Document Checksum:urn:sha-512:b58565d35010bb5a2c8887cbb35415b092024a7a73e6f7832a1b8fcf1193e3f91e700e0fccc15c87bcff437e717fb80865cc94db69ec109472bb056fba2edd28
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