Bio-Ontologies: A Knowledge Representation Resource in Bioinformatics

Gómez Chova, L. and Martí Belenguer, D. and Candel Torres , I. Bio-Ontologies: A Knowledge Representation Resource in Bioinformatics., 2009 . In INTED 2009, Valencia (Spain), 9th-11th March, 2009. [Conference paper]

[img]
Preview
 PDF
INTED09-Galvez.pdf
Download (994kB) | Preview

English abstract

Bioinformatics manages the information that has been gathered in databases since the advent of the molecular biology technological revolution. The successful research is based in interpretations of that information that can be accessed and managed computationally, which is a difficult task. An attempt to solve that problem is to use ontologies. Ontologies are computational formalisations of the knowledge about a given domain, allowing computers to manage the information in a semantic level. In medical informatics, ontologies have been used for a longer period of time to produce controlled lexicons for coding schemes. Bio-ontologies define the basic terms and relations in biological domains and are being used among others, as community reference, as the basis for interoperability between systems, and for search, integration and exchange of biological data. The most successful ontologies applied in Bioinformatics are the ones in the Open Biomedical Ontologies (OBO) project. At the same time, the Web Ontology Language (OWL) is a official proposal for ontologies implementation in the semantic web. In this article, we review the current position in bio-ontologies. We review this trend and what benefits it might bring to ontologies and their use within biomedicine.

Item type: Conference paper
Keywords: Ontologies; Bio-ontologies; Knowledge Representation; Open Biomedical Ontologies; Gene Ontology
Subjects: I. Information treatment for information services > ID. Knowledge representation.
Depositing user: Carmen Galvez
Date deposited: 30 Mar 2009
Last modified: 02 Oct 2014 12:13
URI: http://hdl.handle.net/10760/12904

References

Neumann EK, Miller E, Wilbanks J. What the Semantic Web could do for Life Sciences. Biosilico 2004; 2(6): 228–236.

Hendler J. Agents and the semantic web. IEEE Intelligent Systems Journal 2001; 16:30–37.

Nicholas Gibbins, Stephen Harris, and Nigel Shadbolt. Agent-based semantic web services. Journal of Web Semantics, 1(1): 141–154, 2004.

Stevens RD, Robinson AJ, Goble CA. MyGrid: personalised bioinformatics on the information grid. Bioinformatics 2003;19: i302–i304.

Li Y, Zhong N. Web mining model and its applications for information gathering. Knowledge-Based Systems 2004; 17: 207–217.

Kim J-D, Ohta T, Tateisi Y, Tsujii J. GENIA corpus-a semantically annotated corpus for bio-textmining. Bioinformatics 2003; 19: i180– i182.

Collins F, Green E, Guttmacher A, Guyer M. A vision for the future of genomics research. Nature 2003; 422: 835-847.

GO, The Gene Ontology Consortium. Gene Ontology: tool for the unification of biology. Nature Genetics 2000; 25(l): 25–29.

OBO, Open Biomedical Ontologies; http://obo.sourceforge.net/.

Altman R, et al. RiboWeb: An ontology-based system for collaborative molecular biology. IEEE Intelligent Systems 1999; 14(5): 68–76.

Bada M., et al. A short study on the success of the Gene Ontology. Journal of Web Semantics 2004; 1: 235–240.

Mungall, CJ. Obol: integrating language and meaning in bio-ontologies. Comparative and Functional Genomics 2004; 5(6-7): 509-520.

Lewis S. Gene Ontology: looking backwards and forwards. Genome Biology 2005; 6:103.

The Gene Ontology Consortium. Gene Ontology: tool for the unification of biology. Nature Genetics 2000; 23:25–29.

The FlyBase Consortium. The FlyBase database of the drosophila genome projects and community literature. Nucleic Acid Research 1999; 27:85–88.

Blake JA. The mouse genome database (MGD): expanding genetic and genomic resources for the laboratory mouse. Nucleic Acid Research 2000; 28:108–111.

Christie KR, et al. Saccharomyces Genome Database (GSD) provides tools to identify and analyze sequences from Saccharomyces cerevisiae and related se-quences from other organisms. Nucleic Acid Research 2004; 32:D311–D314.

Jasper R, Uschold MA. Framework for understanding and classifying ontology applications. In: Proceedings of the IJCAI-99 Workshop on Ontologies and Problem-Solving Methods: Lessons Learned and Future Trends 1999.

Grigoris A, Harmelen, FV. Web Ontology Language: OWL. In: Handbook on ontologies (International Handbooks on Information Systems) 2004: 67–92.

Wang X, Gorlitsky R, Almeida JS. From XML to RDF: how semantic web technologies will change the design of ’omic’ standards. Nature Biotechnology 2005; 23(9): 1099–1103.

Lord P, Stevens R, Brass A, Goble C. Investigating semantic similarity measures across the Gene Ontology: the relationship between sequence and annotation. Bioinformatics 2003; 19(10): 1275-1283.

Jakoniene V, Rundqvist D, Lambrix P. A method for similarity-based grouping of biological data. In: Proceedings of the 3rd International Workshop on Data Integration in the Life Sciences 2006; LNBI 4075: 136–151.

Gerstein M. Integrative database analysis in structural genomics. Nature Structural Biology 2000; 7 Suppl: 960-963.


Downloads

Downloads per month over past year

Actions (login required)

View Item View Item
Contact us
Send email Sponsors
Sponsor: AIMS Sponsor: D&P Sponsor: UniNa CAB Get connected
Facebook Facebook Twitter Twitter Linkedin LinkedIn RSS RSS