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Interactive analysis of systems biology molecular expression data

Mingwu Zhang12, Qi Ouyang1, Alan Stephenson3, Michael D Kane3, David E Salt4, Sunil Prabhakar2, John Burgner1, Charles Buck1 and Xiang Zhang5*

Author Affiliations

1 Bindley Bioscience Center, Purdue University, West Lafayette, IN 47907, USA

2 Department of Computer Science, Purdue University, West Lafayette, IN 47907, USA

3 Department of Computer and Information Technology, Purdue University, West Lafayette, IN 47907, USA

4 Department of Horticulture, Purdue University, West Lafayette, IN 47907, USA

5 Department of Chemistry, Center for Regulatory and Environmental Analytical Metabolomics, University of Louisville, Louisville, KY 40292, USA

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BMC Systems Biology 2008, 2:23  doi:10.1186/1752-0509-2-23

Published: 29 February 2008



Systems biology aims to understand biological systems on a comprehensive scale, such that the components that make up the whole are connected to one another and work through dependent interactions. Molecular correlations and comparative studies of molecular expression are crucial to establishing interdependent connections in systems biology. The existing software packages provide limited data mining capability. The user must first generate visualization data with a preferred data mining algorithm and then upload the resulting data into the visualization package for graphic visualization of molecular relations.


Presented is a novel interactive visual data mining application, SysNet that provides an interactive environment for the analysis of high data volume molecular expression information of most any type from biological systems. It integrates interactive graphic visualization and statistical data mining into a single package. SysNet interactively presents intermolecular correlation information with circular and heatmap layouts. It is also applicable to comparative analysis of molecular expression data, such as time course data.


The SysNet program has been utilized to analyze elemental profile changes in response to an increasing concentration of iron (Fe) in growth media (an ionomics dataset). This study case demonstrates that the SysNet software is an effective platform for interactive analysis of molecular expression information in systems biology.