Evaluating Viewpoint Entropy for Ribbon Representation of Protein Structure

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 superfamilies 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 experts in molecular biology to find the best viewpoint of the same protein domains and compared their choices with viewpoint entropy.

[1]  Mateu Sbert,et al.  Viewpoint Selection using Viewpoint Entropy , 2001, VMV.

[2]  Michael S. Bernstein,et al.  Learning Perceptual Kernels for Visualization Design , 2014, IEEE Transactions on Visualization and Computer Graphics.

[3]  R. A. Bradley,et al.  Rank Analysis of Incomplete Block Designs: I. The Method of Paired Comparisons , 1952 .

[4]  M J Tarr,et al.  What Object Attributes Determine Canonical Views? , 1999, Perception.

[5]  Adam Finkelstein,et al.  Perceptual models of viewpoint preference , 2011, TOGS.

[6]  W. Delano.  The PyMOL Molecular Graphics System , 2002 .

[7]  David A. Lee,et al.  New functional families (FunFams) in CATH to improve the mapping of conserved functional sites to 3D structures , 2012, Nucleic Acids Res..

[8]  Samuel Hertig,et al.  A guide to the visual analysis and communication of biomolecular structural data , 2014, Nature Reviews Molecular Cell Biology.

[9]  Mateu Sbert,et al.  Realtime automatic selection of good molecular views , 2006, Comput. Graph..

[10]  Ivan Viola,et al.  Visualization of Biomolecular Structures: State of the Art , 2015, EuroVis.

[11]  Bernhard Preim,et al.  Viewpoint Selection for Intervention Planning , 2007, EuroVis.

[12]  D. Amnon Silverstein,et al.  Efficient method for paired comparison , 2001, J. Electronic Imaging.

[13]  Jeffrey Heer,et al.  Crowdsourcing graphical perception: using mechanical turk to assess visualization design , 2010, CHI.

[14]  B. Rost,et al.  Unexpected features of the dark proteome , 2015, Proceedings of the National Academy of Sciences.

[15]  T. N. Bhat,et al.  The Protein Data Bank , 2000, Nucleic Acids Res..

[16]  Daniel R. Montello,et al.  Spatial Reasoning With External Visualizations: What Matters Is What You See, Not Whether You Interact , 2008, Cogn. Sci..

[17]  Ivan Viola,et al.  Visualization of Biomolecular Structures: State of the Art Revisited , 2017, Comput. Graph. Forum.

[18]  Dimitri Plemenos,et al.  Personalized View Selection of 3D Molecular Proteins , 2010 .

[19]  D. Goodsell,et al.  Visualization of macromolecular structures , 2010, Nature Methods.

[20]  Raanan Fattal,et al.  Image and video upscaling from local self-examples , 2011, TOGS.

[21]  Tien-Tsin Wong,et al.  Sampling with Hammersley and Halton Points , 1997, J. Graphics, GPU, & Game Tools.

[22]  Fabian A. Buske,et al.  Aquaria: simplifying discovery and insight from protein structures , 2015, Nature Methods.

[23]  Gem Stapleton,et al.  Visualizing Sets: An Empirical Comparison of Diagram Types , 2014, Diagrams.

[24]  Rafal Mantiuk,et al.  Comparison of Four Subjective Methods for Image Quality Assessment , 2012, Comput. Graph. Forum.

[25]  Yuriko Takeshima,et al.  A feature-driven approach to locating optimal viewpoints for volume visualization , 2005, VIS 05. IEEE Visualization, 2005..

[26]  Mateu Sbert,et al.  Viewpoint Entropy: A New Tool for Obtaining Good Views of Molecules , 2002, VisSym.

[27]  Mateu Sbert,et al.  Automatic View Selection Using Viewpoint Entropy and its Application to Image‐Based Modelling , 2003, Comput. Graph. Forum.

[28]  Han-Wei Shen,et al.  Dynamic View Selection for Time-Varying Volumes , 2006, IEEE Transactions on Visualization and Computer Graphics.

[29]  Thomas Ertl,et al.  Visualization of molecular structures using state-of-the-art techniques in WebGL , 2014, Web3D '14.