@Article{g.yu_2010,
	title = {GOSemSim: an R package for measuring semantic similarity among GO terms and gene products},
	volume = {26},
	issn = {1367-4803},
	url = {http://bioinformatics.oxfordjournals.org/cgi/content/abstract/26/7/976},
	doi = {10.1093/bioinformatics/btq064},
	abstract = {Summary: The semantic comparisons of Gene Ontology (GO) annotations provide quantitative ways to compute similarities between genes and gene groups, and have became important basis for many bioinformatics analysis approaches. GOSemSim is an R package for semantic similarity computation among GO terms, sets of GO terms, gene products and gene clusters. Four information content (IC)- and a graph-based methods are implemented in the GOSemSim package, multiple species including human, rat, mouse, fly and yeast are also supported. The functions provided by the GOSemSim offer flexibility for applications, and can be easily integrated into high-throughput analysis pipelines. Availability: GOSemSim is released under the GNU General Public License within Bioconductor project, and freely available at http://bioconductor.org/packages/2.6/bioc/html/GOSemSim.html Contact: boxc@bmi.ac.cn; sqwang@bmi.ac.cn Supplementary information: Supplementary information is available at Bioinformatics online.},
	journal = {Bioinformatics},
	author = {Guangchuang Yu and Fei Li and Yide Qin and Xiaochen Bo and Yibo Wu and Shengqi Wang},
	month = April,
	year = {2010},
	note = {PMID: 20179076},
	keywords = {Gene Ontology, Semantic Similarity, Bioconductor},
	pages = {976-978}
}

@Article{wang_new_2007,
	title = {A new method to measure the semantic similarity of GO terms},
	volume = {23},
	issn = {1460-2059},
	url = {http://www.ncbi.nlm.nih.gov/pubmed/17344234},
	doi = {btm087},
	abstract = {MOTIVATION: Although controlled biochemical or biological vocabularies, such as Gene Ontology (GO) (http://www.geneontology.org), address the need for consistent descriptions of genes in different data sources, there is still no effective method to determine the functional similarities of genes based on gene annotation information from heterogeneous data sources. RESULTS: To address this critical need, we proposed a novel method to encode a GO term's semantics (biological meanings) into a numeric value by aggregating the semantic contributions of their ancestor terms (including this specific term) in the GO graph and, in turn, designed an algorithm to measure the semantic similarity of GO terms. Based on the semantic similarities of GO terms used for gene annotation, we designed a new algorithm to measure the functional similarity of genes. The results of using our algorithm to measure the functional similarities of genes in pathways retrieved from the saccharomyces genome database (SGD), and the outcomes of clustering these genes based on the similarity values obtained by our algorithm are shown to be consistent with human perspectives. Furthermore, we developed a set of online tools for gene similarity measurement and knowledge discovery. AVAILABILITY: The online tools are available at: http://bioinformatics.clemson.edu/G-SESAME. SUPPLEMENTARY INFORMATION: http://bioinformatics.clemson.edu/Publication/Supplement/gsp.htm.},
	journal = {Bioinformatics (Oxford, England)},
	author = {James Z Wang and Zhidian Du and Rapeeporn Payattakool and Philip S Yu and Chin-Fu Chen},
	month = may,
	year = {2007},
	note = {PMID: 17344234},
	keywords = {Algorithms,Computational Biology,Databases, Genetic,Humans,Semantics,Terminology as Topic,Vocabulary, Controlled},
	pages = {1274-81}
}

@Article{lord_semantic_2003,
	title = {Semantic similarity measures as tools for exploring the gene ontology},
	issn = {1793-5091},
	url = {http://www.ncbi.nlm.nih.gov/pubmed/12603061},
	doi = {12603061},
	abstract = {Many bioinformatics resources hold data in the form of sequences. Often this sequence data is associated with a large amount of annotation. In many cases this data has been hard to model, and has been represented as scientific natural language, which is not readily computationally amenable. The development of the Gene Ontology provides us with a more accessible representation of some of this data. However it is not clear how this data can best be searched, or queried. Recently we have adapted information content based measures for use with the Gene Ontology (GO). In this paper we present detailed investigation of the properties of these measures, and examine various properties of GO, which may have implications for its future design.},
	journal = {Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing},
	author = {P W Lord and R D Stevens and A Brass and C A Goble},
	year = {2003},
	note = {PMID: 12603061},
	keywords = {Classification,Computational Biology,Databases, Protein,Genomics,Humans,Proteomics,Sequence Alignment},
	pages = {601-12}
}

@article{jiang_semantic_1997,
	title = {Semantic Similarity Based on Corpus Statistics and Lexical Taxonomy},
	url = {http://www.citebase.org/abstract?id=oai:arXiv.org:cmp-lg/9709008},
	journal={Proceedings of 10th International Conference on Research In Computational Linguistics},
	abstract = {This paper presents a new approach for measuring semantic similarity/distance between words and concepts. It combines a lexical taxonomy structure with corpus statistical information so that the semantic distance between nodes in the semantic space constructed by the taxonomy can be better quantified with the computational evidence derived from a distributional analysis of corpus data. Specifically, the proposed measure is a combined approach that inherits the edge-based approach of the edge counting scheme, which is then enhanced by the node-based approach of the information content calculation. When tested on a common data set of word pair similarity ratings, the proposed approach outperforms other computational models. It gives the highest correlation value (r = 0.828) with a benchmark based on human similarity judgements, whereas an upper bound (r = 0.885) is observed when human subjects replicate the same task. Comment: 15 pages, Postscript only},
	author = {Jay J. Jiang and David W. Conrath},
	year = {1997}
}

@article{philip_semantic_1999,
	title = {Semantic Similarity in a Taxonomy: An {Information-Based} Measure and its Application to Problems of Ambiguity in Natural Language},
	volume = {11},
	url = {http://nzdl.sadl.uleth.ca/cgi-bin/library?e=d-00000-00---off-0jair--00-0--0-10-0---0---0prompt-10---4-------0-1l--11-en-50---20-about---00-0-1-00-0-0-11-1-0utfZz-8-00\&cl=CL3.1.11\&d=jair-514\&x=1},
	abstract = {Semantic Similarity in a Taxonomy: An {Information-Based} Measure and its Application to Problems of Ambiguity in Natural Language},
	journal = {Journal of Artificial Intelligence Research},
	author = {Resnik Philip},
	year = {1999},
	pages = {95--130}
}

@article{lin_information-theoretic_1998,
	title = {An {Information-Theoretic} Definition of Similarity},
	url = {http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.55.1832},
	doi = {10.1.1.55.1832},
	journal = {In Proceedings of the 15th International Conference on Machine Learning},
	author = {Dekang Lin},
	year = {1998},
	pages = {296---304}
}

@article{schlicker_new_2006,
	title = {A new measure for functional similarity of gene products based on Gene Ontology},
	volume = {7},
	issn = {1471-2105},
	doi = {1471-2105-7-302},
	abstract = {{BACKGROUND:} Gene Ontology {(GO)} is a standard vocabulary of functional terms and allows for coherent annotation of gene products. These annotations provide a basis for new methods that compare gene products regarding their molecular function and biological role. {RESULTS:} We present a new method for comparing sets of {GO} terms and for assessing the functional similarity of gene products. The method relies on two semantic similarity measures; {simRel} and {funSim.} One measure {(simRel)} is applied in the comparison of the biological processes found in different groups of organisms. The other measure {(funSim)} is used to find functionally related gene products within the same or between different genomes. Results indicate that the method, in addition to being in good agreement with established sequence similarity approaches, also provides a means for the identification of functionally related proteins independent of evolutionary relationships. The method is also applied to estimating functional similarity between all proteins in Saccharomyces cerevisiae and to visualizing the molecular function space of yeast in a map of the functional space. A similar approach is used to visualize the functional relationships between protein families. {CONCLUSION:} The approach enables the comparison of the underlying molecular biology of different taxonomic groups and provides a new comparative genomics tool identifying functionally related gene products independent of homology. The proposed map of the functional space provides a new global view on the functional relationships between gene products or protein families.},
	journal = {{BMC} Bioinformatics},
	author = {Andreas Schlicker and Francisco S Domingues and Jörg Rahnenführer and Thomas Lengauer},
	year = {2006},
	note = {{PMID:} 16776819},
	keywords = {{Algorithms,Artificial} {Intelligence,Databases,} {Protein,Genes,Information} Storage and {Retrieval,Multigene} {Family,Natural} Language {Processing,Proteins,Structure-Activity} {Relationship,User-Computer} {Interface,Vocabulary,} Controlled},
	pages = {302}
}
@book{fellbaum_wordnet:electronic_1998,
	edition = {illustrated edition},
	title = {{WordNet:} An Electronic Lexical Database},
	isbn = {{026206197X}},
	publisher = {The {MIT} Press},
	author = {Christiane Fellbaum},
	month = may,
	year = {1998}
}
