MicroRNA prioritization based on target profile similarities

microRNAs form a complex regulatory network with thousands of target genes. This network is known to suffer specific, but largely elusive, genetic perturbations in various types of disease. Accurate prioritization of microRNAs for each disease type would elucidate those perturbations and so facilitate therapeutic and diagnostic design. The multiple target profiles of microRNAs stemming from various experimental and in silico methods allow the definition of wide range of similarities over microRNAs, but the combined use of these of heterogeneous similarities was not utilized in the gene prioritization approach. Using microRNAs as bases, prioritization with a disease-specific query set of microRNAs is straightforward once a microRNAmicroRNA similarity matrices have been derived. Here we demonstrate the application of a one-class version of the multiple kernel learning framework in order to fuse heterogeneous characteristics of microRNAs. We evaluate the method with breast cancer-specific queries, illustrate its technological aspects, and validate our results not only by standard leave-one-out cross validation, but also with a prospective evaluation.