Inferring gene regression networks with model trees

Background: Novel strategies are required in order to handle the huge amount of data produced by microarray technologies. To infer gene regulatory networks, the first step is to find direct regulatory relationships between genes building the so-called gene co-expression networks. They are typically generated using correlation statistics as pairwise similarity measures. Correlation-based methods are very useful in order to determine whether two genes have a strong global similarity but do not detect local similarities. Results: We propose model trees as a method to identify gene interaction networks. While correlation-based methods analyze each pair of genes, in our approach we generate a single regression tree for each gene from the remaining genes. Finally, a graph from all the relationships among output and input genes is built taking into account whether the pair of genes is statistically significant. For this reason we apply a statistical procedure to control the false discovery r...

• Type: Journal
• Year: 2010
• Where: BMCBI
• Authors: Isabel A. Nepomuceno-Chamorro, Jesús S. Aguilar-Ruiz, José C. Riquelme
• Keywords: BMCBI 2010, Correlation-based Methods, Gene, Linear Regressions