Graduated: December 16, 2017
On Biological Network Visualization: Understanding Challenges, Measuring the Status Quo, and Estimating Saliency of Visual Attributes
Biomedical research increasingly relies on the analysis and visualization of a wide range of collected data. However, for certain research questions, such as those based on the interconnectedness of biological elements, the sheer quantity, complexity, and variety of data may result in rather large and dense networks, rendering them visually uninterpretable. Since networks are important models in biomedicine, and since visualization is a valuable form of analysis, it stands to reason that the biomedical community may benefit from improvements to network visualization.
My dissertation focuses on the following three studies. First, I cover a semi-structured interview study aimed at uncovering the challenges researchers face while analyzing and visualizing biological networks. Second, I describe a systematic review aimed at characterizing visual attributes and assessing the ability to complete selected graph tasks in figures containing node-link diagrams obtained from peer-reviewed bioinformatics literature. Furthermore, I explain the Information Triad, a small conceptual framework I developed to reason about network visualization research questions, followed by a description of visual encoding exploration software I implemented based on the framework. Finally, I detail the design and execution of a task-centered perception study, where the saliency of several visual attributes were estimated as functions for the task of visually scanning a network.
Through these studies, I contributed to the understanding of network-related visualization challenges encountered by researchers, showed that graph figures in bioinformatics literature may be designed for varying purposes, developed a conceptual framework for reasoning about network visualization, built visual encoding software that supports systematic and reproducible explorations of the visual encoding set space, and finally, obtained an estimate of how numerous visual encodings are related to one’s ability to visually scan a network.
Last Known Position:
Data & Applied Scientist, Microsoft
John Gennari (Co-Chair), Neil Abernethy (Co-Chair), Jeffrey M Heer, Abraham David Flaxman (GSR)