This chapter describes methods for learning gene interaction networks from high-throughput gene expression data sets. Many genes have unknown or poorly understood functions and interactions, especially in diseases such as cancer where the genome is frequently mutated. The gene interactions inferred by learning a network model from the data can form the basis of hypotheses that can be verified by subsequent biological experiments. This chapter focuses specifically on Bayesian network models, which have a level of mathematical detail greater than purely conceptual models but less than detailed differential equation models. From a network learning perspective the most severe problem with microarray data is the limited sample size, since there are usually many plausible networks for modeling the system. Since these cannot be reliably distinguished using the number of samples found in current microarray data sets, we describe robust network learning strategies for reducing the number of false interactions detected. We perform preliminary clustering using co-expression network analysis and gene shaving. Subsequently we construct Bayesian networks to obtain a global perspective of the relationships between these gene clusters. Throughout this chapter, we illustrate the concepts being expounded by referring to an ongoing example of a publicly available breast cancer data set.