Annotating gene sets by mining large literature collections with protein networks

Sheng Wang, Jianzhu Ma, Michael Ku Yu, Fan Zheng, Edward W. Huang, Jiawei Han, Jian Peng, Trey Ideker

Research output: Contribution to journalConference articlepeer-review


Analysis of patient genomes and transcriptomes routinely recognizes new gene sets associated with human disease. Here we present an integrative natural language processing system which infers common functions for a gene set through automatic mining of the scientific literature with biological networks. This system links genes with associated literature phrases and combines these links with protein interactions in a single heterogeneous network. Multiscale functional annotations are inferred based on network distances between phrases and genes and then visualized as an ontology of biological concepts. To evaluate this system, we predict functions for gene sets representing known pathways and find that our approach achieves substantial improvement over the conventional text-mining baseline method. Moreover, our system discovers novel annotations for gene sets or pathways without previously known functions. Two case studies demonstrate how the system is used in discovery of new cancer-related pathways with ontological annotations.

Original languageEnglish (US)
Pages (from-to)602-613
Number of pages12
JournalPacific Symposium on Biocomputing
Issue number212669
StatePublished - 2018
Event23rd Pacific Symposium on Biocomputing, PSB 2018 - Kohala Coast, United States
Duration: Jan 3 2018Jan 7 2018


  • Functional annotations
  • Gene interactions
  • Knowledge network
  • Text mining

ASJC Scopus subject areas

  • Biomedical Engineering
  • Computational Theory and Mathematics


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