Representing biomedical knowledge: A view from the philosophy of science

Abstract

Aims and Objectives of the Research We aim at identifying the modelling demands across heterogeneous biomedical fields that intersect in breast cancer research. We describe a methodology that covers various epistemic and pragmatic needs in breast cancer conceptualisation as well as formal modelling, taking into account specifically representational means, expressivity and granularity. Moreover, we propose an ontology model for HER2+ breast cancer phenotypes that integrates molecular and clinical representations.

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which function as the epistemic vehicles for reasoning about the domain’s knowledge targets. In other words, heterogeneous kinds of ‘objects’ represented in an ontology serve the common purpose of representing knowledge. Therefore, a treatment of represented ‘objects’ as epistemic tools justifies their integration into one OR.

Even so, modelling and representing biomedical knowledge involves numerous challenges [ 6,7 ]. The complexity of living systems that are objects of biomedical knowledge emerges as an indispensable problem for the design of biomedical ORs [ 7 ]. In order to address some of the challenges that an ontology model needs to face in establishing a shared conceptualisation [ 1 ], we identify fundamental issues related to modelling the breast cancer domain, e.g. dealing with complexity, heterogeneity, combination of qualitative and quantitative data, mereological and functional representations, as well as various kinds of reasoning within and across domains [ 5 ]. In order to approach this heterogeneity of representations, we invoke the view of SRs as sketched above. As a result, we propose a functional, pragmatic approach as the most productive way to treat these various kinds of ‘objects’ that are constitutive parts of biomedical knowledge, which will eventually be represented as the units of a biomedical OR. Namely, the pragmatics here concerns the specific purpose that represented ‘units’ play in ORs. Each of the units (objects of knowledge) has a particular pragmatic function within a representation. Discussing the case of molecular and clinical breast cancer classification [ 8,9,10,11 ], we further analyse the roles that particular classificatory units play in clinical and molecular practice, e.g. explanatory, predictive etc., satisfying practical needs of the domain. We distinguish the most relevant categories of the classificatory units used to characterise molecular and clinical features of breast cancer, dividing them into normative, quantitative and qualitative categories such as prognosis, therapy response, biomarkers, clinical grading, molecular grading, carcinogenic mechanisms etc. We also specify how the subcategories of each of the groups can be related to the OBO foundry ontologies for representation of biological processes, metrological, mereological and functional features.

Research Questions 1. How can biomedical ontology integrate molecular and clinical knowledge? 2. Can various perspectives, epistemic and pragmatic needs (e.g. in representing breast cancer) be merged into a single ontology model? 3. What are the specific functions and roles that heterogeneous kinds of ‘objects’ play within biomedical knowledge that an ontology aims to represent? 4. How can formal ontology deal with breast cancer knowledge representation?

Research Methodology

Our approach combines methodologies from the philosophy of science with ontology research. We thus analyse scientific methods and practices and discuss their impact on the problem of modelling and representing the breast cancer domain. We moreover attempt to extrapolate from the results of philosophical analysis to the field of applied ontology, specifying a methodology for modelling a breast cancer ontology.

Research Results to Date

We have developed a segment of an ontology for a breast cancer subtype, namely HER2+ breast cancer. We moreover modelled the distinction between 'normal' and 'abnormal' HER2 related phenotypes by combining mereological relations with non-monotonic reasoning based on circumscription. In addition to the formal model, we provided an analysis of the relevant scientific methods, representation and reasoning types, thereby justifying particular choices concerning how to represent classes and relations among them. We host the HER2 ontology at OntoHub (http://ontohub.org/), a new ontology repository with formal semantics and supporting different ontology languages and links between ontologies, enabling various reasoning and interoperability scenarios between ontologies.

References

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