As experimental datasets become increasingly large and complex, the necessity to manage and analyze results becomes paramount. Pathway Studio supports a number of statistical algorithms, deciphering rich datasets for multiple profiling technologies alowing you to:

• Find affected proteins and targets in pathways
• Find key regulators of disease mechanisms, stress responses or cell processes
• Identify the most influential transcription factors or signaling pathways from gene expression data
• Discover novel pathways and networks implicated in disease

Interpret gene expression data within the biological context of protein-protein interactions, pathways and pathway components.

Start with either raw gene expression data, or a list of significant genes and use Pathway Studio to find the answers:

• Are there key regulators that explain my expression changes?
• Is the regulation acting coordinately on downstream targets?
• What key biological processes are regulated in my experiment?
• What proteins should I look at? What do they do?
• What kinases and phosphatases modify proteins in my gene list?

• Broad institute's Gene Set Enrichment Analysis (GSEA) finds pathways affected in the experiment
• Ariadne's unique and proprietary Subnetwork Enrichment Analysis helps identify expression targets, binding partners, disease regulators, etc. to create pathways for the most differentially expressed genes using data from the ResNet database
• Fisher Exact Test helps find the most relevant groups affected in the experiment
• Build Dense Expression Networks searches the ResNet database for dense expressed clusters (only available in Pathway Studio Enterprise)
  

• ResNet Exchange (RNEF) and XML formats for molecular network data
• Simple tab-delimited format for molecular interaction data import from GO and other leading pathway databases
• Flexible tab-delimited format for microarray data import
• Multiple microarray platforms including Affymetrix, Agilent and Illumina