Evaluating viewpoint entropy for ribbon representation of protein structure
Heinrich, J., Vuong, Jenny, Hammang, Chris, Wu, A., Rittenbruch, Markus, Hogan, James, Brereton, Margot, & O'Donoghue, Sean (2016) Evaluating viewpoint entropy for ribbon representation of protein structure. In Ma, K.L., Santucci, G., & Van Wijk, J.J. (Eds.) Proceedings of the 37th Annual Conference of the European Association for Computer Graphics, The Eurographics Association and John Wiley & Sons Ltd., Lisbon, Portugal, pp. 181-190.
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While many measures of viewpoint goodness have been proposed in computer graphics, none have been evaluated for ribbon representations of protein secondary structure. To fill this gap, we conducted a user study on Amazon’s Mechanical Turk platform, collecting human viewpoint preferences from 65 participants for 4 representative su- perfamilies of protein domains. In particular, we evaluated viewpoint entropy, which was previously shown to be a good predictor for human viewpoint preference of other, mostly non-abstract objects. In a second study, we asked 7 molecular biology experts to find the best viewpoint of the same protein domains and compared their choices with viewpoint entropy.
Our results show that viewpoint entropy overall is a significant predictor of human viewpoint preference for ribbon representations of protein secondary structure. However, the accuracy is highly dependent on the complexity of the structure: while most participants agree on good viewpoints for small, non-globular structures with few secondary structure elements, viewpoint preference varies considerably for complex structures. Finally, experts tend to choose viewpoints of both low and high viewpoint entropy to emphasize different aspects of the respective structure.
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|Item Type:||Conference Paper|
|Subjects:||Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > ARTIFICIAL INTELLIGENCE AND IMAGE PROCESSING (080100) > Computer Graphics (080103)
Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > INFORMATION SYSTEMS (080600) > Computer-Human Interaction (080602)
|Divisions:||Current > Schools > School of Design
Current > Schools > School of Electrical Engineering & Computer Science
Current > QUT Faculties and Divisions > Creative Industries Faculty
Current > QUT Faculties and Divisions > Science & Engineering Faculty
|Copyright Owner:||Copyright 2016 The Author(s)|
|Deposited On:||24 Mar 2016 04:24|
|Last Modified:||16 Aug 2016 02:20|
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