When looking at the output of surface classifiers over a corpus of proteins, it can be a daunting task to discover those proteins and regions on protein surfaces that exhibit exceptionally profound or poor predictive performance of the classifier. In this research, we explore the design space of representing data embedded on 3D surfaces as 2D summaries to support rapid identification of corpus elements. We also explore the role of color constancy in computer-generated imagery and if it interferes with color perception. We posit that this overview+detail visualization modified to fit the domain will enable domain experts to analyze data previously difficult to understand.
Related Publications:
Alper Sarikaya, Danielle Albers, Julie C. Mitchell, Michael Gleicher. Visualizing Validation of Protein Surface Classifiers. Computer Graphics Forum, June 2014 (EuroVis ’14).
Alper Sarikaya, Danielle Albers, Michael Gleicher. Understanding Performance of Protein Structural Classifiers. IEEE Visualization Poster Proceedings (SciVis ’13).
Danielle Albers, Alper Sarikaya, Michael Gleicher. Lightness Constancy in Surface Visualization. IEEE Visualization Poster Proceedings (SciVis ’13).