2. A Visual Analytics Approach to Exploring Protein Flexibility Subspaces, Scott Barlowe, Jing Yang, Donald J. Jacobs, Dennis R. Livesay, Jamal Alsakran, Ye Zhao, Deeptak Verma, James Mottonen, Proceedings of IEEE Pacific Visualization, March, 2013, IEEE (LNK)(Bibtex).
Understanding what causes proteins to change shape and how the resulting shape
influences function will expedite the design of more narrowly focused drugs and
therapies. Shape alterations are often the result of flexibility changes in a
set of localized neighborhoods that may or may not act in concert. Computational
models have been developed to predict flexibility changes under varying
empirical parameters. In this paper, we tackle a significant challenge facing
scientists when analyzing outputs of a computational model, namely how to
identify, examine, compare, and group interesting neighborhoods of proteins
under different parameter sets. This is a difficult task since comparisons over
protein subunits that comprise diverse neighborhoods are often too complex to
characterize with a simple metric and too numerous to analyze manually. Here, we
present a series of novel visual analytics approaches toward addressing this
task. User scenarios illustrate the utility of these approaches and feedback
from domain experts confirms their effectiveness.
3. Biomedical volume rendering with Prof.
Arie Kaufman and Prof. Klaus Mueller, Stony Brook University
The Magic Volume Lens: An Interactive Focus+Context Technique for Volume Rendering. Lujing Wang, Ye Zhao, Klaus Mueller and Arie Kaufman, In Proceedings of IEEE Visualization 2005, pages 367-374 (PDF)(Bibtex).
Massive taxi trajectory data is exploited for knowledge discovery in
transportation and urban planning. Existing visual analytics methods typically
retrieve trajectories by selecting geospatial points or regions on a map. The
data queries are facilitated by carefully designed grid or tree structures. In
many real-world tasks, domain and public users want to conveniently search and
analyze taxi trajectories by combined conditions based on semantic information
such as street and POI names, which however, cannot be easily supported by
existing methods. To fill the gap, we develop a novel visual query system of
massive taxi trajectory data directly supporting various visual analytics tasks
over text based searches (i.e. name queries). Taxi trajectories are converted
into “taxi documents” through a transformation process named textualization.
This process can map a taxi’s path into a series of street/POI names and convert
vehicle’s speeds into user-defined descriptive terms. Then a corpora of taxi
documents is formed and indexed to provide flexible name queries and fast
performance. Moreover, we develop a set of visualization tools for interactive
visual analytics of query results. A set of unique tasks and case studies are
presented to show the usability of the system..