Disease Ontology Semantic and Enrichment analysis

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 [Philip, 1999], Jiang [Jiang and Conrath, 1997], Lin [Lin, 1998] and Schlicker [Schlicker et al., 2006], and one graph structure-based method, proposed by Wang [Wang et al., 2007], were implemented. These methods were also implemented in our GOSemSim [Yu et al., 2010] package for measuring GO-term semantic similarities. Hypergeometric test [Boyle et al., 2004] was implemented for enrichment analysis. To start with DOSE package, type following code below:

[1]  Dekang Lin,et al.  An Information-Theoretic Definition of Similarity , 1998, ICML.

[2]  Carole A. Goble,et al.  Semantic Similarity Measures as Tools for Exploring the Gene Ontology , 2002, Pacific Symposium on Biocomputing.

[3]  Yibo Wu,et al.  GOSemSim: an R package for measuring semantic similarity among GO terms and gene products , 2010, Bioinform..

[4]  Pablo Tamayo,et al.  Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles , 2005, Proceedings of the National Academy of Sciences of the United States of America.

[5]  Gang Feng,et al.  From disease ontology to disease-ontology lite: statistical methods to adapt a general-purpose ontology for the test of gene-ontology associations , 2009, Bioinform..

[6]  Gang Feng,et al.  Disease Ontology: a backbone for disease semantic integration , 2011, Nucleic Acids Res..

[7]  David Botstein,et al.  GO: : TermFinder--open source software for accessing Gene Ontology information and finding significantly enriched Gene Ontology terms associated with a list of genes , 2004, Bioinform..

[8]  Philip S. Yu,et al.  A new method to measure the semantic similarity of GO terms , 2007, Bioinform..

[9]  Thomas Lengauer,et al.  A new measure for functional similarity of gene products based on Gene Ontology , 2006, BMC Bioinformatics.

[10]  David W. Conrath,et al.  Semantic Similarity Based on Corpus Statistics and Lexical Taxonomy , 1997, ROCLING/IJCLCLP.

[11]  Philip Resnik,et al.  Semantic Similarity in a Taxonomy: An Information-Based Measure and its Application to Problems of Ambiguity in Natural Language , 1999, J. Artif. Intell. Res..

[12]  Guangchuang Yu,et al.  clusterProfiler: an R package for comparing biological themes among gene clusters. , 2012, Omics : a journal of integrative biology.