Disease gene prioritization plays an important role in disclosing the relation between genes and diseases and it has attracted much research. As a consequence, a high number of disease gene prioritization methods have been proposed. Among them, graph-based methods are the most promising paradigms due to their ability to naturally represent many types of relations using a graph representation. One key factor of success of graph-based learning methods is the definition of a proper graph node similarity measure normally measured by graph node kernels. However, most approaches share two common limitations: first, they are based on the diffusion phenomenon which does not effectively exploit the nodes’ context; second, they are not able to process the auxiliary information associated to graph nodes. 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.

The conjunctive disjunctive graph node kernel for disease gene prioritization

Tran Van, Dinh
Membro del Collaboration Group
;
Sperduti, Alessandro
Membro del Collaboration Group
;
2018

Abstract

Disease gene prioritization plays an important role in disclosing the relation between genes and diseases and it has attracted much research. As a consequence, a high number of disease gene prioritization methods have been proposed. Among them, graph-based methods are the most promising paradigms due to their ability to naturally represent many types of relations using a graph representation. One key factor of success of graph-based learning methods is the definition of a proper graph node similarity measure normally measured by graph node kernels. However, most approaches share two common limitations: first, they are based on the diffusion phenomenon which does not effectively exploit the nodes’ context; second, they are not able to process the auxiliary information associated to graph nodes. 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.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11577/3280229
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