@article{Tran_Sperduti_Backofen-Heter_netwo_integ-2020,
author = {Tran, Van Dinh and Sperduti, Alessandro and Backofen, Rolf 
          and Costa, Fabrizio},
title = {Heterogeneous networks integration for disease-gene 
         prioritization with node kernels},
journal = {Bioinformatics},
year = {2020},
doi = {10.1093/bioinformatics/btaa008},
volume = {36},
user = {alkhanbo},
pmid = {31990289},
pages = {2649-2656},
number = {9},
issn = {1367-4803},
abstract = {MOTIVATION: 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. RESULTS: We propose an approach based on three 
            phases: first, we merge all sources in a single network, 
            then we partition the integrated network according to edge 
            density introducing a notion of edge type to distinguish the 
            parts and finally, we employ a novel node kernel suitable 
            for graphs with typed edges. We show how the node kernel can 
            generate a large number of discriminative features that can 
            be efficiently processed by linear regularized machine 
            learning classifiers. We report state-of-the-art results on 
            12 disease-gene associations and on a time-stamped benchmark 
            containing 42 newly discovered associations. AVAILABILITY 
            AND IMPLEMENTATION: Source code: 
            https://github.com/dinhinfotech/DiGI.git. SUPPLEMENTARY 
            INFORMATION: Supplementary data are available at 
            Bioinformatics online.}
}