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Initial implementation of a comparative data analysis ontology.

Prosdocimi F, Chisham B, Pontelli E, Thompson JD, Stoltzfus A - Evol. Bioinform. Online (2009)

Bottom Line: Using the Web Ontology Language (OWL), we have defined these and other fundamental concepts in a Comparative Data Analysis Ontology (CDAO).CDAO has been evaluated for its ability to represent token data sets and to support simple forms of reasoning.With further development, CDAO will provide a basis for tools (for semantic transformation, data retrieval, validation, integration, etc.) that make it easier for software developers and biomedical researchers to apply evolutionary methods of inference to diverse types of data, so as to integrate this powerful framework for reasoning into their research.

View Article: PubMed Central - PubMed

Affiliation: Department of Structural Biology and Genomics, Institut de Génétique et de Biologie Moléculaire et Cellulaire (IGBMC), F-67400 Illkirch, France.

ABSTRACT
Comparative analysis is used throughout biology. When entities under comparison (e.g. proteins, genomes, species) are related by descent, evolutionary theory provides a framework that, in principle, allows N-ary comparisons of entities, while controlling for non-independence due to relatedness. Powerful software tools exist for specialized applications of this approach, yet it remains under-utilized in the absence of a unifying informatics infrastructure. A key step in developing such an infrastructure is the definition of a formal ontology. The analysis of use cases and existing formalisms suggests that a significant component of evolutionary analysis involves a core problem of inferring a character history, relying on key concepts: "Operational Taxonomic Units" (OTUs), representing the entities to be compared; "character-state data" representing the observations compared among OTUs; "phylogenetic tree", representing the historical path of evolution among the entities; and "transitions", the inferred evolutionary changes in states of characters that account for observations. Using the Web Ontology Language (OWL), we have defined these and other fundamental concepts in a Comparative Data Analysis Ontology (CDAO). CDAO has been evaluated for its ability to represent token data sets and to support simple forms of reasoning. With further development, CDAO will provide a basis for tools (for semantic transformation, data retrieval, validation, integration, etc.) that make it easier for software developers and biomedical researchers to apply evolutionary methods of inference to diverse types of data, so as to integrate this powerful framework for reasoning into their research.

No MeSH data available.


Related in: MedlinePlus

ontology development strategy. The strategy for development of CDAO was modified from that suggested by Stevens et al.32 We began by studying use cases. After deciding on a representation system, we conceptualized domain knowledge by identifying, defining, and classifying terms for key concepts and relations. These concepts and relations were formalized, and then subjected to evaluation as described in the text.
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f1-ebo-2009-047: ontology development strategy. The strategy for development of CDAO was modified from that suggested by Stevens et al.32 We began by studying use cases. After deciding on a representation system, we conceptualized domain knowledge by identifying, defining, and classifying terms for key concepts and relations. These concepts and relations were formalized, and then subjected to evaluation as described in the text.

Mentions: The strategy adopted to develop CDAO (Fig. 1), roughly followed the ontology building life-cycle suggested by Stevens et al.32 We began (Specification step) by considering tasks and use cases, ranging from every-day chores, e.g. sequence alignment, to challenging projects, e.g. comparing developmental gene expression patterns across species.33 At the same time we gathered a list of related artefacts—file formats, database schemas, software interfaces, and so on—that have been proposed or are in use in the evolutionary analysis domain (some are listed in Table 1).


Initial implementation of a comparative data analysis ontology.

Prosdocimi F, Chisham B, Pontelli E, Thompson JD, Stoltzfus A - Evol. Bioinform. Online (2009)

ontology development strategy. The strategy for development of CDAO was modified from that suggested by Stevens et al.32 We began by studying use cases. After deciding on a representation system, we conceptualized domain knowledge by identifying, defining, and classifying terms for key concepts and relations. These concepts and relations were formalized, and then subjected to evaluation as described in the text.
© Copyright Policy - open-access
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC2747124&req=5

f1-ebo-2009-047: ontology development strategy. The strategy for development of CDAO was modified from that suggested by Stevens et al.32 We began by studying use cases. After deciding on a representation system, we conceptualized domain knowledge by identifying, defining, and classifying terms for key concepts and relations. These concepts and relations were formalized, and then subjected to evaluation as described in the text.
Mentions: The strategy adopted to develop CDAO (Fig. 1), roughly followed the ontology building life-cycle suggested by Stevens et al.32 We began (Specification step) by considering tasks and use cases, ranging from every-day chores, e.g. sequence alignment, to challenging projects, e.g. comparing developmental gene expression patterns across species.33 At the same time we gathered a list of related artefacts—file formats, database schemas, software interfaces, and so on—that have been proposed or are in use in the evolutionary analysis domain (some are listed in Table 1).

Bottom Line: Using the Web Ontology Language (OWL), we have defined these and other fundamental concepts in a Comparative Data Analysis Ontology (CDAO).CDAO has been evaluated for its ability to represent token data sets and to support simple forms of reasoning.With further development, CDAO will provide a basis for tools (for semantic transformation, data retrieval, validation, integration, etc.) that make it easier for software developers and biomedical researchers to apply evolutionary methods of inference to diverse types of data, so as to integrate this powerful framework for reasoning into their research.

View Article: PubMed Central - PubMed

Affiliation: Department of Structural Biology and Genomics, Institut de Génétique et de Biologie Moléculaire et Cellulaire (IGBMC), F-67400 Illkirch, France.

ABSTRACT
Comparative analysis is used throughout biology. When entities under comparison (e.g. proteins, genomes, species) are related by descent, evolutionary theory provides a framework that, in principle, allows N-ary comparisons of entities, while controlling for non-independence due to relatedness. Powerful software tools exist for specialized applications of this approach, yet it remains under-utilized in the absence of a unifying informatics infrastructure. A key step in developing such an infrastructure is the definition of a formal ontology. The analysis of use cases and existing formalisms suggests that a significant component of evolutionary analysis involves a core problem of inferring a character history, relying on key concepts: "Operational Taxonomic Units" (OTUs), representing the entities to be compared; "character-state data" representing the observations compared among OTUs; "phylogenetic tree", representing the historical path of evolution among the entities; and "transitions", the inferred evolutionary changes in states of characters that account for observations. Using the Web Ontology Language (OWL), we have defined these and other fundamental concepts in a Comparative Data Analysis Ontology (CDAO). CDAO has been evaluated for its ability to represent token data sets and to support simple forms of reasoning. With further development, CDAO will provide a basis for tools (for semantic transformation, data retrieval, validation, integration, etc.) that make it easier for software developers and biomedical researchers to apply evolutionary methods of inference to diverse types of data, so as to integrate this powerful framework for reasoning into their research.

No MeSH data available.


Related in: MedlinePlus