MINING GENE EXPRESSION DATABASES FOR LOCAL CAUSAL RELATIONSHIPS USING A SIMPLE CONSTRAINT-BASED ALGORITHM

Abstract

There is great potential in mining gene expression microarray databases to discover causal relationships in the gene-regulation pathway. Several methods using Bayesian networks have been reported. Most of them use a heuristics search based on the criteria for choosing a network, but these methods are often computationally intractable for microarray data with thousands of genes. In this work, a simple constrained-based, local causal discovery method is presented. This method is computationally feasible but does not attempt to discover complete causal structure. To show the effectiveness of this method, we have conducted simulations and applied this method to the data set from Hughes et al. from 300 expression profiles of yeast. Using this method, results of simulation data tests demonstrated that the accuracy ratios of causal relationships became higher when the sample size increased. From the yeast data set, a number of causal relations were found. A cursory analysis shows some of the relations have biological sense, others need further investigation.