Characterizing disease states from topological properties of transcriptional regulatory networks

Background: High throughput gene expression experiments yield large amounts of data that can augment our understanding of disease processes, in addition to classifying samples. Here we present new paradigms of data Separation based on construction of transcriptional regulatory networks for normal and abnormal cells using sequence predictions, literature based data and gene expression studies. We analyzed expression datasets from a number of diseased and normal cells, including different types of acute leukemia, and breast cancer with variable clinical outcome. Results: We constructed sample-specific regulatory networks to identify links between transcription factors (TFs) and regulated genes that differentiate between healthy and diseased states. This approach carries the advantage of identifying key transcription factor-gene pairs with differential activity between healthy and diseased states rather than merely using gene expression profiles, thus alluding to processes that may be in...

• Type: Journal
• Year: 2006
• Where: BMCBI
• Authors: David Tuck, Harriet Kluger, Yuval Kluger
• Keywords: BMCBI 2006, Gene, Gene Expression, Regulatory Networks