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Visual Integration of Quantitative Proteomic Data, Pathways, and Protein Interactions
July/August 2010 (vol. 16 no. 4)
pp. 609-620
ASCII Text
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Radu Jianu, Kebing Yu, Lulu Cao, Vinh Nguyen, Arthur R. Salomon, David H. Laidlaw, "Visual Integration of Quantitative Proteomic Data, Pathways, and Protein Interactions," IEEE Transactions on Visualization and Computer Graphics, vol. 16, no. 4, pp. 609-620, July/August, 2010.
 
 
BibTex
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@article{ 10.1109/TVCG.2009.106,
author = {Radu Jianu and Kebing Yu and Lulu Cao and Vinh Nguyen and Arthur R. Salomon and David H. Laidlaw},
title = {Visual Integration of Quantitative Proteomic Data, Pathways, and Protein Interactions},
journal ={IEEE Transactions on Visualization and Computer Graphics},
volume = {16},
number = {4},
issn = {1077-2626},
year = {2010},
pages = {609-620},
doi = {http://doi.ieeecomputersociety.org/10.1109/TVCG.2009.106},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
x 
 
TY - JOUR
JO - IEEE Transactions on Visualization and Computer Graphics
TI - Visual Integration of Quantitative Proteomic Data, Pathways, and Protein Interactions
IS - 4
SN - 1077-2626
SP609
EP620
EPD - 609-620
A1 - Radu Jianu,
A1 - Kebing Yu,
A1 - Lulu Cao,
A1 - Vinh Nguyen,
A1 - Arthur R. Salomon,
A1 - David H. Laidlaw,
PY - 2010
KW - Biological (genome or protein) databases
KW - data and knowledge visualization
KW - graphs and networks
KW - interactive data exploration and discovery
KW - visualization techniques and methodologies.
VL - 16
JA - IEEE Transactions on Visualization and Computer Graphics
ER -
 
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.

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Index Terms:
Biological (genome or protein) databases, data and knowledge visualization, graphs and networks, interactive data exploration and discovery, visualization techniques and methodologies.
Citation:
Radu Jianu, Kebing Yu, Lulu Cao, Vinh Nguyen, Arthur R. Salomon, David H. Laidlaw, "Visual Integration of Quantitative Proteomic Data, Pathways, and Protein Interactions," IEEE Transactions on Visualization and Computer Graphics, vol. 16, no. 4, pp. 609-620, July-Aug. 2010, doi:10.1109/TVCG.2009.106
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