Cancer, which involves the dysregulation of genes via multiple mechanisms, is unlikely to be fully explained by a single data type. By combining different “omes”, researchers can increase the discovery of novel bio-molecular associations with disease-related phenotypes. Investigation of functional relations among genes associated with the same disease condition may further help to develop more accurate disease-relevant prediction models. In this work, we present an integrative framework called Data & Analytic Integrator (DAI), to explore the relationship between different omics via different mathematical formulations and algorithms. In particular, we investigate the combinatorial use of molecular knowledge identified from omics integration methods netDx, iDRW and SSL, by fusing the derived aggregated similarity matrices and by exploiting these in a semi-supervised learner. The analysis workflows were applied to real-life data for ovarian cancer and underlined the benefits of joint data and analytic integration.