Drawing inferences from large, heterogeneous sets of biological data requires a theoretical framework that is capable of representing, e.g. DNA and protein sequences, protein structures, microarray expression data, various types of interaction networks, etc. Recently, a class of algorithms known as kernel methods has emerged as a powerful framework for combining diverse types of data. The support vector machine (SVM) algorithm is the most popular kernel method, due to its theoretical underpinnings and strong empirical performance on a wide variety of classification tasks. Furthermore, several recently described extensions allow the SVM to assign relative weights to various datasets, depending upon their utilities in performing a given classification task.