In this early part of the post-genomic era, the inference of the functions associated with gene products is a necessary first step in understanding the development and maintenance of living cells. We describe the development of a machine learning method for predicting biological process as defined by the gene ontology (GO). The algorithm uses features that can be generated from amino acid sequence alone, and does not require further experimental studies such as microarrays, 2-hybrid screens or systematic 'pull-down' assays. The budding yeast Saccharomyces cerevisiae is used because of its comprehensive set of functional annotations, but the approach is sufficiently general for application to other eukaryote genomes. The input data include phylogenetic profiles, which represent the distribution of orthologous proteins in the genomes of other organisms, position-specific scoring matrices, and secondary structure and dynamic disorder predictions. These are encoded using diffusion kernels, which are used to represent pair-wise relationships such as sequence or secondary structure element similarity between nodes (proteins) in a graph. These kernels are benchmarked on the process prediction problem using a maximal margin (SVM) learning algorithm.