Disease Ontology (DO) provides an open source ontology for the integration of biomedical data that is associated with human disease. DO analysis can lead to interesting discoveries that deserve further clinical investigation.

DOSE was designed for semantic similarity measure and enrichment analysis.

Four information content (IC)-based methods, proposed by Resnik, Jiang, Lin and Schlicker, and one graph structure-based method, proposed by Wang, were implemented. The calculation details can be referred to the vignette of R package GOSemSim.

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In recently years, high-throughput experimental techniques such as microarray and mass spectrometry can identify many lists of genes and gene products. The most widely used strategy for high-throughput data analysis is to identify different gene clusters based on their expression profiles. Another commonly used approach is to annotate these genes to biological knowledge, such as Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG), and identify the statistically significantly enriched categories. These two different strategies were implemented in many bioconductor packages, such as Mfuzz and BHC for clustering analysis and GOstats for GO enrichment analysis.

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第一次写软件包，计算基因和GO的语义相似性。

实现Wang的方法，基于GO的图结构。 以及四种基于信息含量的计算方法（Resnik, Jiang, Lin, Rel）。

可以从Bioconductor下载到。 http://bioconductor.org/packages/release/bioc/html/GOSemSim.html http://bioconductor.org/packages/devel/bioc/html/GOSemSim.html

支持human, rat, mouse, fly, yeast, zebrafish,worm, E coli, arabidopsis等物种。

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