This paper introduces a new method for creating an interactive sequence similarity map of all known influenza virus
protein sequences and integrating the map with existing general purpose analytical tools. The NCBI data model was
designed to provide a high degree of interconnectedness amongst data objects. Substantial and continuous increase in
data volume has led to a large and highly connected information space. Researchers seeking to explore this space are
challenged to identify a starting point. They often choose data that is popular in the literature. Reference in the literature
follow a power law distribution and popular data points may bias explorers toward paths that lead only to a dead-end of
what is already known. To help discover the unexpected we developed an interactive visual analytics system to map the
information space of influenza protein sequence data. The design is motivated by the needs of eScience researchers.
Conference Committee Involvement (4)
Visualization and Data Analysis 2015
9 February 2015 | San Francisco, California, United States
Visualization and Data Analysis 2014
3 February 2014 | San Francisco, California, United States
Visualization and Data Analysis 2013
4 February 2013 | Burlingame, California, United States
Visualization and Data Analysis 2012
23 January 2012 | Burlingame, California, United States
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