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Exploring Normalization and Network Reconstruction Methods using In Silico and In Vivo Models
Citation:
BURGOON, L. Exploring Normalization and Network Reconstruction Methods using In Silico and In Vivo Models. Presented at Agilent Technologies-USEPA Workshop Network Science meets Predictive Exotoxicology: Reverse Engineering Adverse Outcome Pathways, Santa Clara, CA, May 10 - 12, 2010.
Impact/Purpose:
Will discuss our current research, comparing combinations of different single-channel Agilent microarray normalization and network reconstruction methods using our mouse model of dioxin exposure and liver injury.
Description:
Abstract: Lessons learned from the recent DREAM competitions include: The search for the best network reconstruction method continues, and we need more complete datasets with ground truth from more complex organisms. It has become obvious that the network reconstruction methods that perform the best in one year of the DREAM competitions fail in the next. This begs the questions, "Are we creating methods that lack generalizability?" and "Will simpler reconstruction methods prevail?" In this talk, I will discuss our current research, comparing combinations of different single-channel Agilent microarray normalization and network reconstruction methods using our mouse model of dioxin exposure and liver injury. We also exploit the fact that dioxin toxicity requires binding to the aryl hydrocarbon receptor, a ligand-activated transcription factor, to construct our ground truth network using chromatin immunopreciptation (ChIP)-chip data. This abstract does not necessarily reflect the view ofthe Environmental Protection Agency.