Inferring regulatory networks from time series expression data and relational data via inductive logic programming

Determining the underlying regulatory mechanism of genetic networks is one of the central challenges of computational biology. Numerous methods have been developed and applied to the important but complex task of reverse engineering regulatory networks from highthroughput gene expression data. However, many challenges remain. In this paper, we are interested in learning rules that will reveal the causal genes for the expression variation from various relational data sources in addition to gene expression data. Following our previous work where we showed that time series gene expression data could potentially uncover causal effects, we describe an application of an inductive logic programming (ILP) system, to the task of identifying important regulatory relationships from discretized time series gene expression data, protein-protein interaction, protein phosphorylation and transcription factor data about the organism. Specifically, we learn rules for predicting gene expression levels at the next time step based on the available relational data and then generalize the learned theory to visualize a pruned network of important interactions. We evaluate and present experimental results on microarray experiments from Gasch et al on Saccharomyces cerevisiae. © Springer-Verlag Berlin Heidelberg 2007.

Duke Scholars

Author David Page Biostatistics & Bioinformatics, Division of Biostatistics