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Kebing Yu

dblp:26/8300 · DBLP profile ↗
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1ranked-venue papers
0as first author
0since 2021 · last 2010
—ORCID · none

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.

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

Topics — the 4 heaviest of 5, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Bioinformatics and computational biology › protein analysis › protein-protein interaction
protein-protein interaction analysis
0.112010
Visual Integration of Quantitative Proteomic Data, Pathways, and Protein Interactions · IEEE Trans. Vis. Comput. Graph. 2010
Bioinformatics and computational biology
proteomics
0.112010
Visual Integration of Quantitative Proteomic Data, Pathways, and Protein Interactions · IEEE Trans. Vis. Comput. Graph. 2010
Visualization and visual analytics
biomedical visualization
0.112010
Visual Integration of Quantitative Proteomic Data, Pathways, and Protein Interactions · IEEE Trans. Vis. Comput. Graph. 2010
Bioinformatics and computational biology › systems bioinformatics
pathway analysis
0.012010
Visual Integration of Quantitative Proteomic Data, Pathways, and Protein Interactions · IEEE Trans. Vis. Comput. Graph. 2010

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

pathway integration · 0.2network visualization · 0.2
YearPublicationVenuePosition
2010 Visual Integration of Quantitative Proteomic Data, Pathways, and Protein Interactions
abstract
We introduce several novel visualization and interaction paradigms for visual analysis of published protein-protein interaction networks, canonical signaling pathway models, and quantitative proteomic data. We evaluate them anecdotally with domain scientists to demonstrate their ability to accelerate the proteomic analysis process. Our results suggest that structuring protein interaction networks around canonical signaling pathway models, exploring pathways globally and locally at the same time, and driving the analysis primarily by the experimental data, all accelerate the understanding of protein pathways. Concrete proteomic discoveries within T-cells, mast cells, and the insulin signaling pathway validate the findings. The aim of the paper is to introduce novel protein network visualization paradigms and anecdotally assess the opportunity of incorporating them into established proteomic applications. We also make available a prototype implementation of our methods, to be used and evaluated by the proteomic community.
Radu Jianu, Kebing Yu, Lulu Cao, Arthur R. Salomon, David H. Laidlaw
IEEE Trans. Vis. Comput. Graph.2