In this chapter, we discuss a number of approaches to network inference from large-scale functional genomics data. Our goal is to describe current methods that can be used to infer predictive networks. At present, one of the most effective methods to produce networks with predictive value is the Bayesian network approach. This approach was initially instantiated by Friedman et al. and further refined by Eric Schadt and his research group. The Bayesian network approach has the virtue of identifying predictive relationships between genes from a combination of expression and eQTL data. However, the approach does not provide a mechanistic bases for predictive relationships and is ultimately hampered by an inability to model feedback. A challenge for the future is to produce networks that are both predictive and provide mechanistic understanding. To do so, the methods described in several chapters of this book will need to be integrated. Other chapters of this book describe a number of methods to identify or predict network components such as physical interactions. At the end of this chapter, we speculate that some of the approaches from other chapters could be integrated and used to "annotate" the edges of the Bayesian networks. This would take the Bayesian networks one step closer to providing mechanistic "explanations" for the relationships between the network nodes.