Random walk with restart on multiplex and heterogeneous biological networks

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 (RWR) 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 other in the networks.

In this study, we extended the RWR 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 walk can also jump to a network containing different sets of edges and nodes, such as phenotype similarities between diseases. We devised a leave-one-out cross-validation strategy to evaluate the algorithms abilities to predict disease-associated genes. We demonstrate the increased performances of the multiplex-heterogeneous RWR 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 candidate genes for the Wiedemann-Rautenstrauch syndrome, and to explore the network vicinity of the SHORT syndrome.

The source code is available on GitHub at: https://github.com/alberto-valdeolivas/RWR-MH. In addition, an R package is freely available through Bioconductor at: http://bioconductor.org/packages/RandomWalkRestartMH/.

Supplementary data are available at Bioinformatics online.