Abstract
Network analysis provides deep insight into real complex systems. Revealing the link between topological and functional role of network elements can be crucial to understand the mechanisms underlying the system. Here we propose a Cytoscape plugin (GIANT) to perform network clustering and characterize nodes at the light of a modified Guimerà-Amaral cartography. This approach results into a vivid picture of the a topological/functional relationship at both local and global level. The plugin has been already approved and uploaded on the Cytoscape APP store.
Highlights
The network paradigm helps modeling the multiscale character of biological systems: ‘‘networks’’ is the generic name for graphs, which represent a set of nodes linked by edges
Biological networks very often display a scale-free architecture lying halfway between random networks, whose wiring is assigned according to a Gaussian distribution of link probability, and regular networks, whose nodes all show the same degree
They found average Pearson correlation coefficient (APCC) distribution follows a bimodal distribution singling out two distinct hub populations: they called ‘‘party hubs’’ those nodes that are highly correlated in expression with their partners and ‘‘date hubs’’ those showing more limited co-expression with their own partners
Summary
The network paradigm helps modeling the multiscale character of biological systems: ‘‘networks’’ is the generic name for graphs, which represent a set of nodes linked by edges. One of the most challenging tasks for biological scale-free networks analysis is to assign a functional role to each node depending on its location in the network In their innovative work, Han et al [1] estimated the dynamics of hubs (high-degree nodes) from the analysis of messenger RNA expression profiles. The authors examined how much hubs in the yeast interactome are co-expressed with their interaction partners, computing the average Pearson correlation coefficient (APCC) between the hub mRNA expression and its nearest neighbors They found APCC distribution follows a bimodal distribution singling out two distinct hub populations: they called ‘‘party hubs’’ those nodes that are highly correlated in expression with their partners (high APCC) and ‘‘date hubs’’ those showing more limited co-expression with their own partners (lower APCC). The authors showed that a link exists between this hub classification and the network tolerance against node breakdown: scale-free networks are resilient to random node removal (failure), albeit extremely sensitive to the targeted removal of hubs (attack) [2,3]
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