Issue No. 04 - July/August (2010 vol. 16)
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/TVCG.2009.106
Radu Jianu , Brown University, Providence
Kebing Yu , Brown University, Providence
Lulu Cao , Brown University, Providence
Vinh Nguyen , Brown University, Providence
Arthur R. Salomon , Brown University, Providence
David H. Laidlaw , Brown University, Providence
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.
Biological (genome or protein) databases, data and knowledge visualization, graphs and networks, interactive data exploration and discovery, visualization techniques and methodologies.
D. H. Laidlaw, L. Cao, K. Yu, R. Jianu, V. Nguyen and A. R. Salomon, "Visual Integration of Quantitative Proteomic Data, Pathways, and Protein Interactions," in IEEE Transactions on Visualization & Computer Graphics, vol. 16, no. , pp. 609-620, 2009.