Gene-disease associations are inferred on the basis of simi- larities between the proteins encoded by genes. Biological relationships used to define similarities range from interacting proteins, proteins that participate in pathways and protein expression profiles. Though graph ker- nel methods have become a prominent approach for association prediction, most solutions are based on a notion of information diffusion that does not capture the specificity of different network parts. Here we propose a graph kernel method that explicitly models the configuration of each gene’s con- text. An empirical evaluation on several biological databases shows that our proposal is competitive w.r.t. state-of-the-art kernel approaches.

The Conjunctive Disjunctive Node Kernel

Tran Van Dinh;Alessandro Sperduti;
2017

Abstract

Gene-disease associations are inferred on the basis of simi- larities between the proteins encoded by genes. Biological relationships used to define similarities range from interacting proteins, proteins that participate in pathways and protein expression profiles. Though graph ker- nel methods have become a prominent approach for association prediction, most solutions are based on a notion of information diffusion that does not capture the specificity of different network parts. Here we propose a graph kernel method that explicitly models the configuration of each gene’s con- text. An empirical evaluation on several biological databases shows that our proposal is competitive w.r.t. state-of-the-art kernel approaches.
2017
ESANN 2017 proceedings
European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning
978-287587039-1
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11577/3260028
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