title: DOSE: an R/Bioconductor package for disease ontology semantic and enrichment analysis.
abstract: CONCLUSIONS
  Disease ontology (DO) annotates human genes in the context of disease. DO is important annotation in translating molecular findings from high-throughput data to clinical relevance. DOSE is an R package providing semantic similarity computations among DO terms and genes which allows biologists to explore the similarities of diseases and of gene functions in disease perspective. Enrichment analyses including hypergeometric model and gene set enrichment analysis are also implemented to support discovering disease associations of high-throughput biological data. This allows biologists to verify disease relevance in a biological experiment and identify unexpected disease associations. Comparison among gene clusters is also supported.
  
  
  METHODS
  DOSE is released under Artistic-2.0 License. The source code and documents are freely available through Bioconductor (http://www.bioconductor.org/packages/release/bioc/html/DOSE.html).
  
  
  BACKGROUND
  Supplementary data are available at Bioinformatics online.
  
  
  BACKGROUND
  gcyu@connect.hku.hk or tqyhe@jnu.edu.cn.
authors: State Key Laboratory of Emerging Infectious Diseases, School of Public Health, The University of Hong Kong, Hong Kong SAR, Key Laboratory of Functional Protein Research of Guangdong Higher Education Institutes, College of Life Science and Technology, Jinan University, Guangzhou 510632 and Guangdong Information Center, Guangzhou 510031, China State Key Laboratory of Emerging Infectious Diseases, School of Public Health, The University of Hong Kong, Hong Kong SAR, Key Laboratory of Functional Protein Research of Guangdong Higher Education Institutes, College of Life Science and Technology, Jinan University, Guangzhou 510632 and Guangdong Information Center, Guangzhou 510031, China.
authors: Guangchuang
authors: G
authors: Yu
authors: State Key Laboratory of Emerging Infectious Diseases, School of Public Health, The University of Hong Kong, Hong Kong SAR, Key Laboratory of Functional Protein Research of Guangdong Higher Education Institutes, College of Life Science and Technology, Jinan University, Guangzhou 510632 and Guangdong Information Center, Guangzhou 510031, China.
authors: Li-Gen
authors: LG
authors: Wang
authors: State Key Laboratory of Emerging Infectious Diseases, School of Public Health, The University of Hong Kong, Hong Kong SAR, Key Laboratory of Functional Protein Research of Guangdong Higher Education Institutes, College of Life Science and Technology, Jinan University, Guangzhou 510632 and Guangdong Information Center, Guangzhou 510031, China.
authors: Guang-Rong
authors: GR
authors: Yan
authors: State Key Laboratory of Emerging Infectious Diseases, School of Public Health, The University of Hong Kong, Hong Kong SAR, Key Laboratory of Functional Protein Research of Guangdong Higher Education Institutes, College of Life Science and Technology, Jinan University, Guangzhou 510632 and Guangdong Information Center, Guangzhou 510031, China.
authors: Qing-Yu
authors: QY
authors: He
mesh_terms: D019295:Computational Biology / Q000379:methods*; D030541:Databases, Genetic; D004194:Disease / Q000235:genetics*; D063990:Gene Ontology; D006801:Humans; D005810:Multigene Family; D011381:Programming Languages; D012660:Semantics; D012984:Software