The conjunctive disjunctive graph node kernel for disease gene prioritization

In this paper, we propose an efficient graph node kernel, based on graph decompositions, that not only is able to effectively take into account nodes' context, but also to exploit additional information available on graph nodes. The key idea is to learn and generalize from small network fragments present in the neighborhood of genes of interest. An empirical evaluation on several biological databases shows that our proposal achieves state-of-the-art results.