Despite significant advances in omics techniques, the identification of genes causing rare genetic diseases and the understanding of the molecular networks underlying those disorders remains difficult. Gene prioritization attempts to integrate multiple, heterogeneous data sources to identify candidate genes most likely to be associated with or causative for a disorder. Such strategies are useful both to support clinical genetic diagnosis and to speed up biological discovery. Genomic data fusion algorithms are rapidly maturing statistical and machine learning techniques have emerged that integrate complex, heterogeneous information (such as sequence similarity, interaction networks, expression data, annotation, or biomedical literature) towards prioritization, clustering, or prediction. In this talk, we will focus in particular on kernel methods and will propose several strategies for prioritization and clustering in particular. We also go beyond learning methods as such by addressing how such strategies can be embedded into the daily practice of geneticists, mostly through collaborative knowledge bases that integrate tightly with prioritization and network analysis methods.