Compact Integration of Multi-Network Topology for Functional Analysis of Genes.

ABSTRACT: The topological landscape of molecular or functional interaction networks provides a rich source of information for inferring functional patterns of genes or proteins. However, a pressing yet-unsolved challenge is how to combine multiple heterogeneous networks, each having different connectivity patterns, to achieve more accurate inference. Here, we describe the Mashup framework for scalable and robust network integration. In Mashup, the diffusion in each network is first analyzed to characterize the topological context of each node. Next, the high-dimensional topological patterns in individual networks are canonically represented using low-dimensional vectors, one per gene or protein. These vectors can then be plugged into off-the-shelf machine learning methods to derive functional insigh

SUBMITTER: Cho H 

PROVIDER: S-EPMC5225290 | biostudies-literature | 2016 Dec

REPOSITORIES: biostudies-literature