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dc.contributor.authorBarlowe, Scotten_US
dc.contributor.authorLiu, Yujieen_US
dc.contributor.authorYang, Jingen_US
dc.contributor.authorLivesay, Dennis R.en_US
dc.contributor.authorJacobs, Donald J.en_US
dc.contributor.authorMottonen, Jamesen_US
dc.contributor.authorVerma, Deeptaken_US
dc.contributor.editorH. Hauser, H. Pfister, and J. J. van Wijken_US
dc.date.accessioned2014-02-21T20:23:43Z
dc.date.available2014-02-21T20:23:43Z
dc.date.issued2011en_US
dc.identifier.issn1467-8659en_US
dc.identifier.urihttp://dx.doi.org/10.1111/j.1467-8659.2011.01949.xen_US
dc.description.abstractThe knowledge gained from biology datasets can streamline and speed-up pharmaceutical development. However, computational models generate so much information regarding protein behavior that large-scale analysis by traditional methods is almost impossible. The volume of data produced makes the transition from data to knowledge difficult and hinders biomedical advances. In this work, we present a novel visual analytics approach named WaveMap for exploring data generated by a protein flexibility model. WaveMap integrates wavelet analysis, visualizations, and interactions to facilitate the browsing, feature identification, and comparison of protein attributes represented by two-dimensional plots. We have implemented a fully working prototype of WaveMap and illustrate its usefulness through expert evaluation and an example scenario.en_US
dc.publisherThe Eurographics Association and Blackwell Publishing Ltd.en_US
dc.subjectI.5.5 [Pattern Recognition]en_US
dc.subjectImplementationen_US
dc.subjectInteractive Systemsen_US
dc.titleWaveMap: Interactively Discovering Features From Protein Flexibility Matrices Using Wavelet-based Visual Analyticsen_US
dc.description.seriesinformationComputer Graphics Forumen_US
dc.description.volume30en_US
dc.description.number3en_US


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