title: Measuring semantic similarities by combining gene ontology annotations and gene co-function networks.
abstract: BACKGROUND
  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 from gene 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 http://www.msu.edu/~jinchen/NETSIM .
authors.affiliation: School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China. jiajiepeng@hit.edu.cn.|Department of Energy Plant Research Laboratory, Michigan State University, East Lansing, MI, 48824, USA. jiajiepeng@hit.edu.cn.
authors.forename: Jiajie
authors.initials: J
authors.lastname: Peng
authors.affiliation: Department of Energy Plant Research Laboratory, Michigan State University, East Lansing, MI, 48824, USA. uygunsah@msu.edu.|Genetics Program, Michigan State University, East Lansing, MI, 48824, USA. uygunsah@msu.edu.
authors.forename: Sahra
authors.initials: S
authors.lastname: Uygun
authors.affiliation: Department of Plant Biology, Carnegie Institution for Science, 260 Panama St, Stanford, CA, 94305, USA. thkim04@gmail.com.
authors.forename: Taehyong
authors.initials: T
authors.lastname: Kim
authors.affiliation: School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China. ydwang@hit.edu.cn.
authors.forename: Yadong
authors.initials: Y
authors.lastname: Wang
authors.affiliation: Department of Plant Biology, Carnegie Institution for Science, 260 Panama St, Stanford, CA, 94305, USA. srhee@carnegiescience.edu.
authors.forename: Seung Y
authors.initials: SY
authors.lastname: Rhee
authors.affiliation: Department of Energy Plant Research Laboratory, Michigan State University, East Lansing, MI, 48824, USA. jinchen@msu.edu.|Department of Computer Science and Engineering, Michigan State University, East Lansing, MI, 48824, USA. jinchen@msu.edu.
authors.forename: Jin
authors.initials: J
authors.lastname: Chen
mesh_terms: D000465:Algorithms; D029681:Arabidopsis Proteins / Q000235:genetics; D019295:Computational Biology / Q000379:methods*; D003577:Cytochrome P-450 Enzyme System / Q000235:genetics; D063990:Gene Ontology; D053263:Gene Regulatory Networks; D006801:Humans; D053858:Metabolic Networks and Pathways; D058977:Molecular Sequence Annotation; D005810:Multigene Family; D029701:Saccharomyces cerevisiae Proteins / Q000235:genetics; D012660:Semantics; D012984:Software; D018875:Vocabulary, Controlled
chemical_list: D029681:Arabidopsis Proteins; D029701:Saccharomyces cerevisiae Proteins; D003577:Cytochrome P-450 Enzyme System