'''de novo Generation, Visualization, and Analysis of Biological Networks using Cytoscape and Agilent Literature Search'''

Allan Kuchinsky Agilent Technologies, Santa Clara, California, USA

Analyzing high-throughput experimental data in the context of biological processes can be a daunting task. For example, analysis of microarray data is useful only in identifying statistically significant gene expression changes, whereas identifying discriminatory pathways/networks of gene interactions from a set of significant molecules can provide critical information for understanding complex processes and identifying therapeutic targets from among the potentially large list of differentially expressed genes. I will present a systems-based method to analyze high-throughput data for studying complex diseases, based upon literature-based de novo network construction, supplemented by visualization techniques for examining generated networks against experimental data.

Central to our approach are two software systems:

• Cytoscape, an open source bioinformatics software platform for visualizing molecular interaction networks and integrating these interactions with gene expression profiles and other state data. (www.cytoscape.org).
• Agilent Literature Search, a meta-search tool for automatically querying multiple text-based search engines. Computationally extracted associations are grouped into a network that is viewed and manipulated in Cytoscape. Available as a Cytoscape plugin.

I will provide an in-depth look at Cytoscape and Agilent Literature Search and discusses their application in collaborative studies with researchers at the Stanford University School of Medicine in the area of cardiovascular disease.

Data keywords: gene expression, scientific text, unstructured data sources, increased resolution of data analysis Cytoscape keywords: connectivity analysis, data overlay, multi-datapoint visualization (heatstrips)