Paper Info
Title
The latent geometry of the human protein interaction network.
Abstract
Motivation: A series of recently introduced algorithms and models advocates for the existence of a hyperbolic geometry underlying the network representation of complex systems. Since the human protein interaction network (hPIN) has a complex architecture, we hypothesized that uncovering its latent geometry could ease challenging problems in systems biology, translating them into measuring distances between proteins. Results: We embedded the hPIN to hyperbolic space and found that the inferred coordinates of nodes capture biologically relevant features, like protein age, function and cellular localization. This means that the representation of the hPIN in the two-dimensional hyperbolic plane offers a novel and informative way to visualize proteins and their interactions. We then used these coordinates to compute hyperbolic distances between proteins, which served as likelihood scores for the prediction of plausible protein interactions. Finally, we observed that proteins can efficiently communicate with each other via a greedy routing process, guided by the latent geometry of the hPIN. We show that these efficient communication channels can be used to determine the core members of signal transduction pathways and to study how system perturbations impact their efficiency. Availability and implementation: An R implementation of our network embedder is available at https://github.com/galanisl/NetHypGeom. Also, a web tool for the geometric analysis of the hPIN accompanies this text at http://cbdm-01.zdv.uni-mainz.de/similar to galanisl/gapi. Contact: galanisl@uni-mainz.de or andrade@uni-mainz.de Supplementary information: Supplementary data are available at Bioinformatics online.
Year
DOI
Venue
2018
10.1093/bioinformatics/bty206
BIOINFORMATICS
Field
DocType
Volume
Embedding,Scale invariance,Biology,Hyperbolic coordinates,Hyperbolic space,Communication channel,Systems biology,Interaction network,Bioinformatics,Geometry,Cluster analysis
Journal
34
Issue
ISSN
Citations 
16
1367-4803
2
PageRank 
References 
Authors
0.37
17
3
Name
Order
Citations
PageRank
Gregorio Alanis-Lobato171.47
Pablo Mier242.10
Miguel A Andrade-Navarro322015.60