CHAPTER 1 GENE REGULATORY NETWORK IDENTIFICATION WITH QUALITATIVE PROBABILISTIC NETWORKS In this chapter, we explore the use of qualitative probabilistic networks (QPNs) in constructing gene regulatory networks from microarray expression data. The chapter aims at demonstrating the usefulness of QPNs in aid Dynamic Bayesian Networks (DBNs) in the process of learning the structure of gene regulatory net- works from microarray gene expression data. We present a study which shows that QPNS deﬁne monotonic relations that are capable of identifying regulatory inter- actions in a manner that is less susceptible to the many sources of uncertainty that surround gene expression data. Moreover, we construct a model that maps the reg- ulatory interactions of genetic networks to QPN constructs and show its capability in providing a set of candidate regulators for target genes, which is subsequently used to establish a prior structure that the DBN learning algorithm can use and which: 1) distinguishes spurious correlations from true regulations; 2) enables the discovery of sets of co-regulators of target genes; 3) results in a more eﬃcient con- struction of gene regulatory networks. The model is compared to existing literature using the known gene regulatory interactions of Drosophila Melanogaster. Gene Regulatory Network Identiﬁcation with Qualitative Probabilistic Networks. By Zina M. Ibrahim, Alioune Ngom and Ahmed Y. Tawﬁk Copyright c  2011 John Wiley & Sons, Inc. 1