Abstract
Biological networks are highly modular and contain a large number of clusters, which are often associated with a specific biological function or disease. Identifying these clusters, or modules, is therefore valuable, but it is not trivial. In this article we propose a recursive method based on the Louvain algorithm for community detection and the PageRank algorithm for authoritativeness weighting in networks. PageRank is used to initialise the weights of nodes in the biological network; the Louvain algorithm with the Newman-Girvan criterion for modularity is then applied to the network to identify modules. Any identified module with more than k nodes is further processed by recursively applying PageRank and Louvain, until no module contains more than k nodes (where k is a parameter of the method, no greater than 100). This method is evaluated on a heterogeneous set of six biological networks from the Disease Module Identification DREAM Challenge. Empirical findings suggest that the method is effective in identifying a large number of significant modules, although with substantial variability across restarts of the method.
Highlights
Biological functions emerge from interactions at the molecular level
Biological networks such as protein-protein interaction (PPI) or regulatory networks have a high degree of modularity where the ‘modules’ often correspond to genes or proteins that are involved in the same biological functions
On the final challenge leaderboard, our solution ranked 12th overall with 44 significant modules identified across the six networks
Summary
For instance our circadian clock relies on the interactions between a large number of genes and proteins[1,2], and many cancer types are typically associated with specific genetic[3] and epigenetic[4] modifications Biological networks such as protein-protein interaction (PPI) or regulatory networks have a high degree of modularity (a measure of strength of the division of the network into subgroups, or clusters, called modules in our context) where the ‘modules’ often correspond to genes or proteins that are involved in the same biological functions. The identification of these disease modules is a valuable tool to identify disease pathways, and to predict other disease genes This task is sometimes known as community detection or graph clustering. A large number of methods exist (see e.g., 6), but there was a lack of common evaluation on relevant biological networks
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