Hilary Coon

dblp:235/3028 · DBLP profile ↗
← Back
1ranked-venue papers
0as first author
0since 2021 · last 2019
0000-0002-8877-5446ORCID · reported

Domains — the database's venue-derived domains; a paper can count in several

Graphics, computer vision, multimedia, augmented reality and games · 1

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Computer graphics and multimedia
1 paper
Visualization and visual analytics · 100%
Interdisciplinary, comprehensive, and emerging computing
1 paper
Bioinformatics and computational biology · 100%

Topics — the 1 heaviest of 2, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Visualization and visual analytics › graph visualization
multivariate graph visualization
0.412019
Lineage: Visualizing Multivariate Clinical Data in Genealogy Graphs · IEEE Trans. Vis. Comput. Graph. 2019

Methods — techniques the papers use, named apart from their topics

graph layout · 0.8data-driven aggregation · 0.8
YearPublicationVenuePosition
2019 Lineage: Visualizing Multivariate Clinical Data in Genealogy Graphs
abstract
The majority of diseases that are a significant challenge for public and individual heath are caused by a combination of hereditary and environmental factors. In this paper we introduce Lineage, a novel visual analysis tool designed to support domain experts who study such multifactorial diseases in the context of genealogies. Incorporating familial relationships between cases with other data can provide insights into shared genomic variants and shared environmental exposures that may be implicated in such diseases. We introduce a data and task abstraction, and argue that the problem of analyzing such diseases based on genealogical, clinical, and genetic data can be mapped to a multivariate graph visualization problem. The main contribution of our design study is a novel visual representation for tree-like, multivariate graphs, which we apply to genealogies and clinical data about the individuals in these families. We introduce data-driven aggregation methods to scale to multiple families. By designing the genealogy graph layout to align with a tabular view, we are able to incorporate extensive, multivariate attributes in the analysis of the genealogy without cluttering the graph. We validate our designs by conducting case studies with our domain collaborators.
Carolina Nobre, Nils Gehlenborg, Hilary Coon, Alexander Lex
IEEE Trans. Vis. Comput. Graph.3