A Bayesian network model of proteins' association with promyelocytic leukemia (PML) nuclear bodies.

The modularity that nuclear organization brings has the potential to explain the function of aggregates of proteins and RNA. Promyelocytic leukemia nuclear bodies are implicated in important regulatory processes. To understand the complement of proteins associated with these intra-nuclear bodies, we construct a Bayesian network model that integrates sequence and protein-protein interaction data. The model predicts association with promyelocytic leukemia nuclear bodies accurately when interaction data is available. At a false positive rate of 10%, the true positive rate is almost 50%, indicated by an independent nuclear proteome reference set. The model provides strong support for further expanding the protein complement with several important regulators and a richer functional repertoire. Using special support vector machine (SVM)-nodes (equipped with string kernels), the Bayesian network is also able to produce predictions on the basis of sequence only, with an accuracy superior to that of baseline models. Supplementary Material is available online at www.liebertonline.com.