Heterogeneous networks integration for disease-gene prioritization with node kernels

Dinh Van Tran, Alessandro Sperduti, Rolf Backofen, Fabrizio Costa

Abstract: The identification of disease–gene associations is a task of fundamental importance in human health research. A typical approach consists in first encoding large gene/protein relational datasets as networks due to the natural and intuitive property of graphs for representing objects’ relationships and then utilizing graph-based techniques to prioritize genes for successive low-throughput validation assays. Since different types of interactions between genes yield distinct gene networks, there is the need to integrate different heterogeneous sources to improve the reliability of prioritization systems.