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Open biomedical pluralism: formalising knowledge about breast cancer phenotypes

Overview of attention for article published in Journal of Biomedical Semantics, September 2012
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1 tweeter

Citations

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Title
Open biomedical pluralism: formalising knowledge about breast cancer phenotypes
Published in
Journal of Biomedical Semantics, September 2012
DOI 10.1186/2041-1480-3-s2-s3
Pubmed ID
Authors

Aleksandra Sojic, Aleksandra Sojic, Oliver Kutz

Abstract

We demonstrate a heterogeneity of representation types for breast cancer phenotypes and stress that the characterisation of a tumour phenotype often includes parameters that go beyond the representation of a corresponding empirically observed tumour, thus reflecting significant functional features of the phenotypes as well as epistemic interests that drive the modes of representation. Accordingly, the represented features of cancer phenotypes function as epistemic vehicles aiding various classifications, explanations, and predictions. In order to clarify how the plurality of epistemic motivations can be integrated on a formal level, we give a distinction between six categories of human agents as individuals and groups focused around particular epistemic interests. We analyse the corresponding impact of these groups and individuals on representation types, mapping and reasoning scenarios. Respecting the plurality of representations, related formalisms, expressivities and aims, as they are found across diverse scientific communities, we argue for a pluralistic ontology integration. Moreover, we discuss and illustrate to what extent such a pluralistic integration is supported by the distributed ontology language DOL, a meta-language for heterogeneous ontology representation that is currently under standardisation as ISO WD 17347 within the OntoIOp (Ontology Integration and Interoperability) activity of ISO/TC 37/SC 3. We particularly illustrate how DOL supports representations of parthood on various levels of logical expressivity, mapping of terms, merging of ontologies, as well as non-monotonic extensions based on circumscription allowing a transparent formal modelling of the normal/abnormal distinction in phenotypes.

Twitter Demographics

The data shown below were collected from the profile of 1 tweeter who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 21 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Mexico 1 5%
United States 1 5%
Germany 1 5%
Ghana 1 5%
Unknown 17 81%

Demographic breakdown

Readers by professional status Count As %
Researcher 6 29%
Student > Ph. D. Student 2 10%
Professor > Associate Professor 2 10%
Professor 2 10%
Student > Master 2 10%
Other 4 19%
Unknown 3 14%
Readers by discipline Count As %
Computer Science 4 19%
Medicine and Dentistry 3 14%
Agricultural and Biological Sciences 3 14%
Philosophy 2 10%
Psychology 2 10%
Other 1 5%
Unknown 6 29%

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 20 October 2012.
All research outputs
#13,978,796
of 17,520,445 outputs
Outputs from Journal of Biomedical Semantics
#293
of 358 outputs
Outputs of similar age
#105,087
of 143,418 outputs
Outputs of similar age from Journal of Biomedical Semantics
#13
of 15 outputs
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