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 co

SUBMITTER: Alanis-Lobato G 

PROVIDER: S-EPMC6084611 | biostudies-literature | 2018 Aug

REPOSITORIES: biostudies-literature