Measuring semantic similarities by combining gene ontology annotations and gene co-function networks

Jiajie Peng, Sahra Uygun, Taehyong Kim, Yadong Wang, Seung Y. Rhee, Jin Chen

Research output: Contribution to journalArticlepeer-review

41 Scopus citations


Gene Ontology (GO) has been used widely to study functional relationships between genes. The current semantic similarity measures rely only on GO annotations and GO structure. This limits the power of GO-based similarity because of the limited proportion of genes that are annotated to GO in most organisms. Results: We introduce a novel approach called NETSIM (network-based similarity measure) that incorporates information fromgene co-function networks in addition to using the GO structure and annotations. Using metabolic reaction maps of yeast, Arabidopsis, and human, we demonstrate that NETSIM can improve the accuracy of GO term similarities. We also demonstrate that NETSIM works well even for genomes with sparser gene annotation data. We applied NETSIM on large Arabidopsis gene families such as cytochrome P450 monooxygenases to group the members functionally and show that this grouping could facilitate functional characterization of genes in these families. Conclusions: Using NETSIM as an example, we demonstrated that the performance of a semantic similarity measure could be significantly improved after incorporating genome-specific information. NETSIM incorporates both GO annotations and gene co-function network data as a priori knowledge in the model. Therefore, functional similarities of GO terms that are not explicitly encoded in GO but are relevant in a taxon-specific manner become measurable when GO annotations are limited. Supplementary information and software are available at

Original languageEnglish
Article number44
JournalBMC Bioinformatics
Issue number1
StatePublished - Dec 14 2015

Bibliographical note

Publisher Copyright:
© 2015 Peng et al.


  • Co-Function network
  • Gene function annotation
  • Gene ontology
  • Semantic similarity

ASJC Scopus subject areas

  • Structural Biology
  • Biochemistry
  • Molecular Biology
  • Computer Science Applications
  • Applied Mathematics


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