EDBT 2026 Demo / reviewers in the wild / expert
Aaron Bryden
dblp:77/10238
· DBLP profile ↗
1ranked-venue papers
1as first author
0since 2021 · last 2012
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Graphics, computer vision, multimedia, augmented reality and games · 1 · 1 first-author
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Interdisciplinary, comprehensive, and emerging computing
1 paper |
Bioinformatics and computational biology · 100% | |
| Computer graphics and multimedia
1 paper |
Visualization and visual analytics · 100% |
Topics — the 3 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Bioinformatics and computational biology
molecular flexibility |
0.1 | 1 | 2012 | Automated Illustration of Molecular Flexibility · IEEE Trans. Vis. Comput. Graph. 2012 |
Bioinformatics and computational biology
structural biology |
0.1 | 1 | 2012 | Automated Illustration of Molecular Flexibility · IEEE Trans. Vis. Comput. Graph. 2012 |
Visualization and visual analytics › scientific visualization
molecular visualization |
0.1 | 1 | 2012 | Automated Illustration of Molecular Flexibility · IEEE Trans. Vis. Comput. Graph. 2012 |
Methods — techniques the papers use, named apart from their topics
normal mode analysis · 0.3glyph design · 0.3affine velocity modeling · 0.3
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2012 | Automated Illustration of Molecular FlexibilityabstractIn this paper, we present an approach to creating illustrations of molecular flexibility using normal mode analysis (NMA). The output of NMA is a collection of points corresponding to the locations of atoms and associated motion vectors, where a vector for each point is known. Our approach abstracts the complex object and its motion by grouping the points, models the motion of each group as an affine velocity, and depicts the motion of each group by automatically choosing glyphs such as arrows. Affine exponentials allow the extrapolation of nonlinear effects such as near rotations and spirals from the linear velocities. Our approach automatically groups points by finding sets of neighboring points whose motions fit the motion model. The geometry and motion models for each group are used to determine glyphs that depict the motion, with various aspects of the motion mapped to each glyph. We evaluated the utility of our system in real work done by structural biologists both by utilizing it in our own structural biology work and quantitatively measuring its usefulness on a set of known protein conformation changes. Additionally, in order to allow ourselves and our collaborators to effectively use our techniques we integrated our system with commonly used tools for molecular visualization. Aaron Bryden, George N. Phillips Jr., Michael Gleicher |
IEEE Trans. Vis. Comput. Graph. | 1 |