"Finding Patterns in a Sea of Data: How Information Visualization Can Support NCI's Fight Against Cancer"
and Ketan Mane
The National Cancer Institute (NCI) speaker series will include talks on July 20 by SLIS faculty member Katy Börner, and by Ben Shneiderman faculty member from the University of Maryland. The series Informatics in Action 2006 is held in Bethesda, Maryland with the goal of "improving the outcomes in implementing health, medical, and bioinformatics technologies through the science of user-centered informatics research." [event website]
ABSTRACT [event website]
Finding Patterns in a Sea of Data: How Information Visualization Can Support NCI's Fight Against Cancer
"This second event of the series will focus on innovations from the field of Information Visualization and how we can apply them at NCI to help solve a problem facing many of us today: managing and interacting with mountains of data. Information Visualization combines computer science and human-computer interaction research to display vast amounts of data visually, interestingly and interactively. It's more than charts and graphs — it provides powerful visual overviews of data patterns that allow you to zoom in, filter out unessential data, adjust which correlations to view, and call up the details you want, in an instant, all in one screen.
Experts will present several examples applying Information Visualization techniques in cutting- edge areas such as:
- comparing chromosomal genetic bits
- viewing and analyzing biological data as networks
- maps that show cancer mortality data by many dimensions at once
- visionary data analysis of children's cancer treatment results."
Börner directs the Information Visualization Laboratory and the Cyberinfrastructure for Network Science Center, Indiana University at IU. SLIS doctoral candidate Ketan Mane also will attend to present a demonstration on his work on Information Visualization on medical diagnostics. Using cancer patient data, the work demonstrates how visualization can play a critical role in identifying medical conditions that cause relapse in patients. Further, the work also shows use of data interactions to support doctors in their data analysis process and help them gain better data insights on patient medical conditions.
Posted July 12, 2006