TITLE

Use artificial neural network to align biological ontologies

AUTHOR(S)
Jingshan Huang; Jiangbo Dang; Huhns, Michael N.; Zheng, W. Jim
PUB. DATE
January 2008
SOURCE
BMC Genomics;2008 Supplement 2, Vol. 9, Special section p1
SOURCE TYPE
Academic Journal
DOC. TYPE
Article
ABSTRACT
Background: Being formal, declarative knowledge representation models, ontologies help to address the problem of imprecise terminologies in biological and biomedical research. However, ontologies constructed under the auspices of the Open Biomedical Ontologies (OBO) group have exhibited a great deal of variety, because different parties can design ontologies according to their own conceptual views of the world. It is therefore becoming critical to align ontologies from different parties. During automated/semi-automated alignment across biological ontologies, different semantic aspects, i.e., concept name, concept properties, and concept relationships, contribute in different degrees to alignment results. Therefore, a vector of weights must be assigned to these semantic aspects. It is not trivial to determine what those weights should be, and current methodologies depend a lot on human heuristics. Results: In this paper, we take an artificial neural network approach to learn and adjust these weights, and thereby support a new ontology alignment algorithm, customized for biological ontologies, with the purpose of avoiding some disadvantages in both rule-based and learning-based aligning algorithms. This approach has been evaluated by aligning two real-world biological ontologies, whose features include huge file size, very few instances, concept names in numerical strings, and others. Conclusion: The promising experiment results verify our proposed hypothesis, i.e., three weights for semantic aspects learned from a subset of concepts are representative of all concepts in the same ontology. Therefore, our method represents a large leap forward towards automating biological ontology alignment.
ACCESSION #
35701850

 

Related Articles

  • AMMO-Prot: amine system project 3D-model finder. Navas-Delgado, Ismael; Montañez, Raúl; Pino-Ángeles, Almudena; Moya-García, Aurelio A.; Urdiales, José Luis; Sánchez-Jiménez, Francisca; Aldana-Montes, José F. // BMC Bioinformatics;2008 Supplement 4, Vol. 9, Special section p1 

    Background: Amines are biogenic amino acid derivatives, which play pleiotropic and very important yet complex roles in animal physiology. For many other relevant biomolecules, biochemical and molecular data are being accumulated, which need to be integrated in order to be effective in the...

  • Identification of putative domain linkers by a neural network -- application to a large sequence database. Miyazaki, Satoshi; Kuroda, Yutaka; Yokoyama, Shigeyuki // BMC Bioinformatics;2006, Vol. 7, p1 

    Background: The reliable dissection of large proteins into structural domains represents an important issue for structural genomics/proteomics projects. To provide a practical approach to this issue, we tested the ability of neural network to identify domain linkers from the SWISSPROT database...

  • RDFScape: Semantic Web meets Systems Biology. Splendiani, Andrea // BMC Bioinformatics;2008 Supplement 4, Vol. 9, Special section p1 

    Background: The recent availability of high-throughput data in molecular biology has increased the need for a formal representation of this knowledge domain. New ontologies are being developed to formalize knowledge, e.g. about the functions of proteins. As the Semantic Web is being introduced...

  • CSO validator: improving manual curation workflow for biological pathways. Jeong, Euna; Nagasaki, Masao; Ikeda, Emi; Sekiya, Yayoi; Saito, Ayumu; Miyano, Satoru // Bioinformatics;Sep2011, Vol. 27 Issue 17, p2471 

    Summary: Manual curation and validation of large-scale biological pathways are required to obtain high-quality pathway databases. In a typical curation process, model validation and model update based on appropriate feedback are repeated and requires considerable cooperation of scientists. We...

  • A common layer of interoperability for biomedical ontologies based on OWL EL. Hoehndorf, Robert; Dumontier, Michel; Oellrich, Anika; Wimalaratne, Sarala; Rebholz-Schuhmann, Dietrich; Schofield, Paul; Gkoutos, Georgios V. // Bioinformatics;Apr2011, Vol. 27 Issue 7, p1001 

    Motivation: Ontologies are essential in biomedical research due to their ability to semantically integrate content from different scientific databases and resources. Their application improves capabilities for querying and mining biological knowledge. An increasing number of ontologies is being...

  • OLSVis: an animated, interactive visual browser for bio-ontologies.  // BMC Bioinformatics;2012, Vol. 13 Issue 1, p116 

    The article offers information on OLSVis computer software.

  • VIZ-GRAIL: visualizing functional connections across disease loci. Raychaudhuri, Soumya // Bioinformatics;Jun2011, Vol. 27 Issue 11, p1589 

    Motivation: As disease loci are rapidly discovered, an emerging challenge is to identify common pathways and biological functionality across loci. Such pathways might point to potential disease mechanisms. One strategy is to look for functionally related or interacting genes across genetic loci....

  • GoWeb: a semantic search engine for the life science web. Dietze, Heiko; Schroeder, Michael // BMC Bioinformatics;2009 Supplement 10, Vol. 10, Special section p1 

    Background: Current search engines are keyword-based. Semantic technologies promise a next generation of semantic search engines, which will be able to answer questions. Current approaches either apply natural language processing to unstructured text or they assume the existence of structured...

  • KA-SB: from data integration to large scale reasoning. Roldán-García, María del Mar; Navas-Delgado, Ismael; Kerzazi, Amine; Chniber, Othmane; Molina-Castro, Joaquín; Aldana-Montes, José F. // BMC Bioinformatics;2009 Supplement 10, Vol. 10, Special section p1 

    Background: The analysis of information in the biological domain is usually focused on the analysis of data from single on-line data sources. Unfortunately, studying a biological process requires having access to disperse, heterogeneous, autonomous data sources. In this context, an analysis of...

Share

Read the Article

Courtesy of THE LIBRARY OF VIRGINIA

Sorry, but this item is not currently available from your library.

Try another library?
Sign out of this library

Other Topics