RT Journal Article
SR Electronic
T1 Random Walk with Restart on Multiplex and Heterogeneous Biological Networks
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 134734
DO 10.1101/134734
A1 Alberto Valdeolivas
A1 Laurent Tichit
A1 Claire Navarro
A1 Sophie Perrin
A1 Gaëlle Odelin
A1 Nicolas Levy
A1 Pierre Cau
A1 Elisabeth Remy
A1 Anaïs Baudot
YR 2017
UL http://biorxiv.org/content/early/2017/05/05/134734.abstract
AB Recent years have witnessed an exponential growth in the number of identified interactions between biological molecules. These interactions are usually represented as large and complex networks, calling for the development of appropriated tools to exploit the functional information they contain. Random walk with restart is the state-of-the-art guilt-by-association approach. It explores the network vicinity of gene/protein seeds to study their functions, based on the premise that nodes related to similar functions tend to lie close to each others in the networks.In the present study, we extended the random walk with restart algorithm to multiplex and heterogeneous networks. The walk can now explore different layers of physical and functional interactions between genes and proteins, such as protein-protein interactions and co-expression associations. In addition, the extended algorithm is able to consider heterogeneous networks; i.e., the walk can also jump to a network containing different sets of nodes and edges, such as phenotype similarities between diseases.We devised a leave-one-out cross-validation strategy to evaluated the algorithms in the prediction of disease-associated genes. We demonstrated the increased performances of the multiplex-heterogeneous random walk with restart as compared to several random walks on monoplex or heterogeneous networks. Overall, our framework is able to leverage the different interaction sources to outperform current approaches.Finally, we applied the algorithm to predict genes candidate for being involved in the Wiedemann-Rautenstrauch syndrome, and to explore the network vicinity of the SHORT syndrome.The source code and the software are freely available at: https://github.com/alberto-valdeolivas/RWR-MH.