Imperial College London
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DrJohnPinney

Central Faculty, Graduate School

Teaching Fellow- Data Science Skills Leader
 
 
 
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Contact

 

+44 (0)20 7594 8629j.pinney

 
 
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Location

 

327Sherfield BuildingSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@article{MacPherson:2009:10.1186/1471-2105-10-95,
author = {MacPherson, JI and Pinney, JW and Robertson, DL},
doi = {10.1186/1471-2105-10-95},
journal = {BMC Bioinformatics},
title = {JNets: exploring networks by integrating annotation.},
url = {http://dx.doi.org/10.1186/1471-2105-10-95},
volume = {10},
year = {2009}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - BACKGROUND: A common method for presenting and studying biological interaction networks is visualization. Software tools can enhance our ability to explore network visualizations and improve our understanding of biological systems, particularly when these tools offer analysis capabilities. However, most published network visualizations are static representations that do not support user interaction. RESULTS: JNets was designed as a network visualization tool that incorporates annotation to explore the underlying features of interaction networks. The software is available as an application and a configurable applet that can provide a flexible and dynamic online interface to many types of network data. As a case study, we use JNets to investigate approved drug targets present within the HIV-1 Human protein interaction network. Our software highlights the intricate influence that HIV-1 has on the host immune response. CONCLUSION: JNets is a software tool that allows interaction networks to be visualized and studied remotely, from within a standard web page. Therefore, using this free software, network data can be presented in an enhanced, interactive format. More information about JNets is available at http://www.manchester.ac.uk/bioinformatics/jnets.
AU  - MacPherson,JI
AU  - Pinney,JW
AU  - Robertson,DL
DO  - 10.1186/1471-2105-10-95
PY  - 2009///
SN  - 1471-2105
TI  - JNets: exploring networks by integrating annotation.
T2  - BMC Bioinformatics
UR  - http://dx.doi.org/10.1186/1471-2105-10-95
VL  - 10
ER  -
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