Big Mechanism Visualization

Angus Forbes, Assistant Professor, Computational Media, University of California, Santa Cruz
Angus Forbes, Assistant Professor, Computational Media, University of California, Santa Cruz
Speaker Name: 
Angus Forbes
Speaker Title: 
Assistant Professor, Computational Media
Speaker Organization: 
Jack Baskin School Of Engineering
Start Time: 
Wednesday, May 16, 2018 - 3:30pm
End Time: 
Wednesday, May 16, 2018 - 4:30pm
Location: 
E2, Room 475
Organizer: 
Angela Brooks
Abstract:

In this talk, I will present a series of biological data visualization projects that provide novel ways to explore protein-protein interaction networks. ReactionFlow presents a novel network visualization to make it easier to identify causal relationships in biological pathways. Dynamic Influence Networks displays an interactive summary of temporal network dynamics based on simulations generated using the Kappa language for rule-based modeling. TimeArcs highlights PubMed articles that provide supporting (or contradicting) evidence for biochemical processes within particular contexts. BranchingSets facilitates the interactive inspection of proteins and protein complexes, minimizing the visual clutter that can occur when visualizing densely-interconnected networks. Some of these techniques may be also useful for visualizing and analyzing genomic data, and part of the talk will involve a discussion of the challenges in visualizing your data.

 

Bio:

Angus Forbes is an Assistant Professor in the Computational Media Department at University of California, Santa Cruz, where he directs UCSC Creative Coding. His research investigates novel techniques for visualizing and interacting with complex scientific information, and his interactive artwork has been featured at museums, galleries, and festivals throughout the world. Angus is a PI on a project funded by DARPA’s Big Mechanism program, for which he developed a series of visualization tools to help identify the causal fragments found in individual PubMed papers and assemble them into causal models. More information about Angus’ work can be found at https://creativecoding.soe.ucsc.edu/projects.php. Contact Professor Forbes at angus@ucsc.edu