An integrative approach to infer regulation programs in a transcription regulatory module network

The module network method, a special type of Bayesian network algorithms, has been proposed to infer transcription regulatory networks from gene expression data. In this method, a module represents a set of genes, which have similar expression prof les and are regulated by same transcription factors. Compared to standard Bayesian network algorithms, this design signif cantly reduces the number of parameters to be learned, and consequently leads to more accurate inferences. The process of learning module networks consists of two steps: f rst clustering genes into modules and then inferring the regulation program (transcription factors) of each module. Many algorithms have been designed to infer the regulation program of a given gene module (i.e., the second step in learning module networks), and these algorithms show very different biases in detecting regulatory relationships. In this work, we explore the possibility of integrating results from different algorithms. The integration methods we select are union, intersection, and weighted rank aggregation. The experiments in a yeast dataset shows that the union and weighted rank aggregation methods produce more accurate predictions than those given by individual algorithms, whereas the intersection method does not yield any improvement in the accuracy of predictions. In addition, somewhat surprisingly, the union method, which has a lower computational cost than rank aggregation, archives comparable results as given by rank aggregation.