Curating a Large-Scale Regulatory Network by Evaluating Its Consistency with Expression Datasets

Abstract. The analysis of large-scale regulatory models using data issued from genome-scale high-throughput experimental techniques is an actual challenge in the systems biology field. This kind of analysis faces three common problems: the size of the model, the uncertainty in the expression datasets, and the heterogeneity of the data. On that account, we propose a method that analyses large-scale networks with small

• Type: Conference
• Year: 2008
• Where: CIBB
• Authors: Carito Guziolowski, Jeremy Gruel, Ovidiu Radulescu, Anne Siegel
• Keywords: Artificial Intelligence, CIBB 2008, Expression Datasets, Heterogeneous Expression Datasets, Regulatory Knowledge