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 (DSD), we design a new embedding-based link prediction method called GLIDE (Global and Local Integrated Diffusion Embedding). GLIDE is designed to effectively capture global network structure, combined with alternative network type-specific customized measures that capture local network structure. We test GLIDE on a collection of three recently curated human biological networks derived from the 2016 DREAM disease module identification challenge as well as a classical version of the yeast PPI network in rigorous cross validation experiments.

Results: We indeed find that different local network structure is dominant in different types of biological networks. We find that the simple local network measures are dominant in the highly connected network core between hub genes, but that GLIDE's global embedding measure adds value in the rest of the network. For example, we make GLIDE-based link predictions from genes known to be involved in Crohn's disease, to genes that are not known to have an association, and make some new predictions, finding support in other network data and the literature.

Join the meeting in Sococo VH 209. Login: tuftscs.sococo.com

Join Zoom Meeting: https://tufts.zoom.us/j/98610939077

PASSWORD: see colloquia email

Dial by your location: +1 312 626 6799 US (Chicago)

Meeting ID: 986 1093 9077

Passcode: see colloquia email

Find your local number: https://tufts.zoom.us/u/adS4koag4r