GLIDE: combining local methods and diffusion state embeddings to predict missing interactions in biological networks.

ABSTRACT:

Motivation

One of the core problems in the analysis of biological networks is the link prediction problem. In particular, existing interactions networks are noisy and incomplete snapshots of the true network, with many true links missing because those interactions have not yet been experimentally observed. Methods to predict missing links have been more extensively studied for social than for biological networks; it was recently argued that there is some special structure in protein-protein interaction (PPI) network data that might mean that alternate methods may outperform the best methods for social networks. Based on a generalization of the diffusion state distance, we design a new embedding-based link prediction method called global and local integrated diffusion embedding (GLI

SUBMITTER: Devkota K 

PROVIDER: S-EPMC7355260 | biostudies-literature | 2020 Jul

REPOSITORIES: biostudies-literature