Integrating genome and functional genomics data to reveal perturbed signaling pathways in ovarian cancers. Proceedings of AMIA Summit on Translational Bioinformatics
Cancers are genetic diseases, driven by somatic mutations that perturb cellular signaling systems. In this study, we aim to reveal the signal transduction pathways that are perturbed by mutations in ovarian cancer. Our approach searches for genetic mutations that lead to a common cellular response, e.g., differential expression of a set of functional related genes. To this end, we first developed a knowledge mining approach to identify functional expression modules; we then developed a graph-based data mining approach to identify mutations that are highly related to the functional modules, as a means to re-constitute signal pathways. Our results indicate that unification of knowledge mining with data mining significantly enhance identification of potential signaling pathways in ovarian cancers.