During the past decade, the new focus on genomics has highlighted a particular challenge: to integrate the different views of the genome that are provided by various types of experimental data. This paper describes a computational framework for integrating and drawing inferences from a collection of genome-wide measurements. Each data set is represented via a kernel function, which defines generalized similarity relationships between pairs of entities, such as genes or proteins. The kernel representation is both flexible and efficient, and can be applied to many different types of data. Furthermore, kernel functions derived from different types of data can be combined in a straightforward fashion -- recent advances in the theory of kernel methods have provided efficient algorithms to perform such combinations in an optimal way. These methods formulate the problem of optimal kernel combination as a convex optimization problem that can be solved with semi-definite programming techniques. In this paper, we demonstrate the utility of these techniques by investigating the problem of predicting membrane proteins from heterogeneous data, including amino acid sequences, hydropathy profiles, gene expression data and known protein-protein interactions. A statistical learning algorithm trained from all of these data performs significantly better than the same algorithm trained on any single type of data and better than existing algorithms for membrane protein classification.