Name
Title | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1109/TVCG.2009.106 | IEEE Trans. Vis. Comput. Graph. |
Keywords | Field | DocType |
protein interaction network,interaction paradigm,protein interactions,novel protein network visualization,established proteomic application,quantitative proteomic data,proteomic analysis process,proteomic community,concrete proteomic discovery,pathway validate,pathway model,visual integration,proteins,signal analysis,data mining,visual analysis,bioinformatics,data visualization,prototypes,genomics,concrete,insulin signaling,data visualisation,signaling pathway,proteomics,acceleration | Data science,Data visualization,Protein–protein interaction,Protein Interaction Networks,Experimental data,Insulin signal transduction pathway and regulation of blood glucose,Proteomics,Visualization,Computer science,Genomics,Theoretical computer science,Computational biology | Journal |
Volume | Issue | ISSN |
16 | 4 | 1077-2626 |
Citations | PageRank | References |
6 | 0.45 | 17 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Radu Jianu | 1 | 126 | 9.90 |
| Kebing Yu | 2 | 6 | 0.45 |
| Lulu Cao | 3 | 6 | 0.45 |
| Vinh Nguyen | 4 | 80 | 8.79 |
| Arthur R Salomon | 5 | 15 | 1.25 |
| David H. Laidlaw | 6 | 1781 | 234.58 |