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A framework for assessing the consistency of drug classes across sources.

Winnenburg R, Bodenreider O - J Biomed Semantics (2014)

Bottom Line: Our work is the first attempt to align drug classes with sophisticated instance-based techniques, while also distinguishing between equivalence and inclusion relations.Additionally, it is the first application of aligning drug classes in ATC and MeSH.By providing a detailed account of similarities and differences between drug classes across sources, our framework has the prospect of effectively supporting the creation of a mapping of drug classes between ATC and MeSH by domain experts.

View Article: PubMed Central - HTML - PubMed

Affiliation: Lister Hill National Center for Biomedical Communications, National Library of Medicine, Bethesda, MD, USA.

ABSTRACT

Background: The objective of this study is to develop a framework for assessing the consistency of drug classes across sources, such as MeSH and ATC. Our framework integrates and contrasts lexical and instance-based ontology alignment techniques. Moreover, we propose metrics for assessing not only equivalence relations, but also inclusion relations among drug classes.

Results: We identified 226 equivalence relations between MeSH and ATC classes through the lexical alignment, and 223 through the instance-based alignment, with limited overlap between the two (36). We also identified 6,257 inclusion relations. Discrepancies between lexical and instance-based alignments are illustrated and discussed.

Conclusions: Our work is the first attempt to align drug classes with sophisticated instance-based techniques, while also distinguishing between equivalence and inclusion relations. Additionally, it is the first application of aligning drug classes in ATC and MeSH. By providing a detailed account of similarities and differences between drug classes across sources, our framework has the prospect of effectively supporting the creation of a mapping of drug classes between ATC and MeSH by domain experts.

No MeSH data available.


Integration of MeSH and ATC through the equivalence and inclusion relations obtained through our framework.
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Figure 3: Integration of MeSH and ATC through the equivalence and inclusion relations obtained through our framework.

Mentions: The equivalence and inclusion relations obtained through our framework can be combined in order to integrate the hierarchical structures of two drug classifications, such as MeSH and ATC. These additional relations create bridges across the original classifications, yielding an emerging hierarchy that combines both of them. As an illustration, we integrated the classes related to alkylating agents in MeSH and ATC. As depicted in FigureĀ 3, all 4th-level classes under Alkylating Agents (L01A) in ATC have inclusion mappings to Antineoplastic Agents, Alkylating and Alkylating Agents in MeSH. The 3rd-level ATC class Alkylating Agents (L01A) itself is found to be equivalent to these two classes in MeSH and is included in their parent classes, Antineoplastic Agents and Toxic Actions, respectively. The 2nd-level ATC class Antineoplastic Agents (L01) can be regarded as equivalent to one of these parents, namely Antineoplastic Agents, although the equivalence score ES is slightly under the threshold of 0.5. Such a representation helps users make sense of the similarities and differences in the organizational structure of the classifications.


A framework for assessing the consistency of drug classes across sources.

Winnenburg R, Bodenreider O - J Biomed Semantics (2014)

Integration of MeSH and ATC through the equivalence and inclusion relations obtained through our framework.
© Copyright Policy - open-access
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4120719&req=5

Figure 3: Integration of MeSH and ATC through the equivalence and inclusion relations obtained through our framework.
Mentions: The equivalence and inclusion relations obtained through our framework can be combined in order to integrate the hierarchical structures of two drug classifications, such as MeSH and ATC. These additional relations create bridges across the original classifications, yielding an emerging hierarchy that combines both of them. As an illustration, we integrated the classes related to alkylating agents in MeSH and ATC. As depicted in FigureĀ 3, all 4th-level classes under Alkylating Agents (L01A) in ATC have inclusion mappings to Antineoplastic Agents, Alkylating and Alkylating Agents in MeSH. The 3rd-level ATC class Alkylating Agents (L01A) itself is found to be equivalent to these two classes in MeSH and is included in their parent classes, Antineoplastic Agents and Toxic Actions, respectively. The 2nd-level ATC class Antineoplastic Agents (L01) can be regarded as equivalent to one of these parents, namely Antineoplastic Agents, although the equivalence score ES is slightly under the threshold of 0.5. Such a representation helps users make sense of the similarities and differences in the organizational structure of the classifications.

Bottom Line: Our work is the first attempt to align drug classes with sophisticated instance-based techniques, while also distinguishing between equivalence and inclusion relations.Additionally, it is the first application of aligning drug classes in ATC and MeSH.By providing a detailed account of similarities and differences between drug classes across sources, our framework has the prospect of effectively supporting the creation of a mapping of drug classes between ATC and MeSH by domain experts.

View Article: PubMed Central - HTML - PubMed

Affiliation: Lister Hill National Center for Biomedical Communications, National Library of Medicine, Bethesda, MD, USA.

ABSTRACT

Background: The objective of this study is to develop a framework for assessing the consistency of drug classes across sources, such as MeSH and ATC. Our framework integrates and contrasts lexical and instance-based ontology alignment techniques. Moreover, we propose metrics for assessing not only equivalence relations, but also inclusion relations among drug classes.

Results: We identified 226 equivalence relations between MeSH and ATC classes through the lexical alignment, and 223 through the instance-based alignment, with limited overlap between the two (36). We also identified 6,257 inclusion relations. Discrepancies between lexical and instance-based alignments are illustrated and discussed.

Conclusions: Our work is the first attempt to align drug classes with sophisticated instance-based techniques, while also distinguishing between equivalence and inclusion relations. Additionally, it is the first application of aligning drug classes in ATC and MeSH. By providing a detailed account of similarities and differences between drug classes across sources, our framework has the prospect of effectively supporting the creation of a mapping of drug classes between ATC and MeSH by domain experts.

No MeSH data available.