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

Hyunghoon Cho, Bonnie Berger, Jian Peng

Research output: Contribution to journalArticlepeer-review

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 insights about genes or proteins. We present tools based on Mashup that achieve state-of-the-art performance in three diverse functional inference tasks: protein function prediction, gene ontology reconstruction, and genetic interaction prediction. Mashup enables deeper insights into the structure of rapidly accumulating and diverse biological network data and can be broadly applied to other network science domains.

Original languageEnglish (US)
Pages (from-to)540-548.e5
JournalCell Systems
Volume3
Issue number6
DOIs
StatePublished - Dec 21 2016

Keywords

  • dimensionality reduction
  • drug response prediction
  • gene function prediction
  • gene ontology reconstruction
  • genetic interaction prediction
  • heterogeneous networks
  • interactome analysis
  • network diffusion
  • network integration

ASJC Scopus subject areas

  • Pathology and Forensic Medicine
  • Histology
  • Cell Biology

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