Open biomedical pluralism: formalising knowledge about breast cancer phenotypes

Area of Science:

• Biomedical Informatics
• Ontology Engineering
• Cancer Research

Background:

• Breast cancer phenotype characterization involves diverse representation types beyond empirical observation.
• These representations reflect functional features and epistemic interests driving scientific inquiry.
• Integrating diverse epistemic motivations is crucial for robust cancer phenotype analysis.

Purpose of the Study:

• To demonstrate the heterogeneity of breast cancer phenotype representations.
• To formally integrate diverse epistemic motivations and representation types.
• To advocate for and illustrate pluralistic ontology integration using the Distributed Ontology Language (DOL).

Main Methods:

• Analysis of representation types for breast cancer phenotypes.
• Distinction of six categories of human agents based on epistemic interests.
• Formal analysis of ontology integration using the Distributed Ontology Language (DOL).

Main Results:

• Breast cancer phenotype characterization encompasses features beyond empirical observation, serving epistemic goals.
• A framework is proposed to integrate diverse epistemic interests and representation types.
• The Distributed Ontology Language (DOL) supports pluralistic ontology integration, including parthood, term mapping, and non-monotonic reasoning for phenotype distinctions.

Conclusions:

• A pluralistic approach to ontology integration is necessary to accommodate diverse scientific representations of breast cancer phenotypes.
• The Distributed Ontology Language (DOL) provides a formal foundation for such integration, enhancing interoperability and reasoning capabilities.
• This work facilitates more comprehensive and accurate modeling of cancer phenotypes by respecting representational diversity.