Gene prioritization through geometric-inspired kernel data fusion

In biology there is often the need to discover the most promising genes, among a large list of candidate genes, to further investigate. While a single data source might not be effective enough, integrating several complementary genomic data sources leads to more accurate prediction. We propose a kernel-based gene prioritization framework using geometric kernel fusion which we have recently developed as a powerful tool for protein fold classification [I]. It has been shown that taking more involved geometry means of their corresponding kernel matrices is less sensitive in dealing with complementary and noisy kernel matrices compared to standard multiple kernel learning methods. Since genomic kernels often encodes the complementary characteristics of biological data, this leads us to research the application of geometric kernel fusion in the gene prioritization task. We utilize an unbiased and prospective benchmark based on the OMIM [2] associations. Experimental results on our prospective benchmark show that our model can improve the accuracy of the state-of-the-art gene prioritization model.