Determination of strongly overlapping signaling activity from microarray data

Background: As numerous diseases involve errors in signal transduction, modern therapeutics often target proteins involved in cellular signaling. Interpretation of the activity of signaling pathways during disease development or therapeutic intervention would assist in drug development, design of therapy, and target identification. Microarrays provide a global measure of cellular response, however linking these responses to signaling pathways requires an analytic approach tuned to the underlying biology. An ongoing issue in pattern recognition in microarrays has been how to determine the number of patterns (or clusters) to use for data interpretation, and this is a critical issue as measures of statistical significance in gene ontology or pathways rely on proper separation of genes into groups. Results: Here we introduce a method relying on gene annotation coupled to decompositional analysis of global gene expression data that allows us to estimate specific activity on strongly couple...

• Type: Journal
• Year: 2006
• Where: BMCBI
• Authors: Ghislain Bidaut, Karsten Suhre, Jean-Michel Claverie, Michael F. Ochs
• Keywords: BMCBI 2006, Decompositional Analysis, Gene, Gene Ontology