title: An integrative approach for measuring semantic similarities using gene ontology.
abstract: BACKGROUND
  Gene Ontology (GO) provides rich information and a convenient way to study gene functional similarity, which has been successfully used in various applications. However, the existing GO based similarity measurements have limited functions for only a subset of GO information is considered in each measure. An appropriate integration of the existing measures to take into account more information in GO is demanding.
  
  
  RESULTS
  We propose a novel integrative measure called InteGO2 to automatically select appropriate seed measures and then to integrate them using a metaheuristic search method. The experiment results show that InteGO2 significantly improves the performance of gene similarity in human, Arabidopsis and yeast on both molecular function and biological process GO categories.
  
  
  CONCLUSIONS
  InteGO2 computes gene-to-gene similarities more accurately than tested existing measures and has high robustness. The supplementary document and software are available at http://mlg.hit.edu.cn:8082/.
authors.forename: Jiajie
authors.initials: J
authors.lastname: Peng
authors.forename: Hongxiang
authors.initials: H
authors.lastname: Li
authors.forename: Qinghua
authors.initials: Q
authors.lastname: Jiang
authors.forename: Yadong
authors.initials: Y
authors.lastname: Wang
authors.forename: Jin
authors.initials: J
authors.lastname: Chen
mesh_terms: D000465:Algorithms; D017360:Arabidopsis / Q000235:genetics; D001185:Artificial Intelligence; D063990:Gene Ontology; D009323:Natural Language Processing; D010363:Pattern Recognition, Automated / Q000379:methods*; D012441:Saccharomyces cerevisiae / Q000235:genetics; D012660:Semantics; D012984:Software; D018511:Systems Integration