Towards an ontological representation of resistance: The case of MRSA

Goldfain, Albert; Cowell, Lindsay G.

doi:10.1016/j.jbi.2010.02.008

Cited by 14 publications

(10 citation statements)

References 8 publications

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“…Under the first heading, Merrill sees some of the principles of ontological realism roughly along the lines of ‘How could such a strange amalgam of Aristotelico-Australian philosophical ideas possibly have import for the workings of serious scientific research?’ In fact, however, since the authors of this communication first began to collaborate in 2002, our ontology development methodology has been driven by needs and concerns not of philosophers, but rather of scientists building systems in areas such as hospital adverse event reporting (Ceusters et al, 2009, 2009a, 2009b), salivaomics (Ai et al, 2010), or the diagnosis and treatment of Methicillin-resistant Staphylococcus aureus (Goldfain et al, 2010). …”

Section: Bfo Dolce Sumo Cycmentioning

confidence: 99%

Ontological realism: A methodology for coordinated evolution of scientific ontologies

Smith¹,

Ceusters²

2010

Applied Ontology

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200

119

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Since 2002 we have been testing and refining a methodology for ontology development that is now being used by multiple groups of researchers in different life science domains. Gary Merrill, in a recent paper in this journal, describes some of the reasons why this methodology has been found attractive by researchers in the biological and biomedical sciences. At the same time he assails the methodology on philosophical grounds, focusing specifically on our recommendation that ontologies developed for scientific purposes should be constructed in such a way that their terms are seen as referring to what we call universals or types in reality. As we show, Merrill’s critique is of little relevance to the success of our realist project, since it not only reveals no actual errors in our work but also criticizes views on universals that we do not in fact hold. However, it nonetheless provides us with a valuable opportunity to clarify the realist methodology, and to show how some of its principles are being applied, especially within the framework of the OBO (Open Biomedical Ontologies) Foundry initiative.

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Section: Bfo Dolce Sumo Cycmentioning

confidence: 99%

Ontological realism: A methodology for coordinated evolution of scientific ontologies

Smith¹,

Ceusters²

2010

Applied Ontology

Self Cite

200

119

View full text Add to dashboard Cite

show abstract

“…For example, endemicity that was a disposition in IDOMAL will be changed to quality of a population , and so will be the terms holoendemicity , hypoendemicity and mesoendemicity , which are absent from IDO. We have also changed resistance from quality to disposition ; although a good case was made for the fact that resistance is a disposition [ 19 ], we should nevertheless state here that resistance is in most, if not all cases a genetic phenotype. And without going into further discussions, we simply state that phenotypes are usually considered to be quality , possibly because of the fact that several of them are visible ( e.g.…”

Section: Resultsmentioning

confidence: 99%

IDOMAL: the malaria ontology revisited

et al. 2013

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BackgroundWith about half a billion cases, of which nearly one million fatal ones, malaria constitutes one of the major infectious diseases worldwide. A recently revived effort to eliminate the disease also focuses on IT resources for its efficient control, which prominently includes the control of the mosquito vectors that transmit the Plasmodium pathogens. As part of this effort, IDOMAL has been developed and it is continually being updated.FindingsIn addition to the improvement of IDOMAL’s structure and the correction of some inaccuracies, there were some major subdomain additions such as a section on natural products and remedies, and the import, from other, higher order ontologies, of several terms, which were merged with IDOMAL terms. Effort was put on rendering IDOMAL fully compatible as an extension of IDO, the Infectious Disease Ontology. The reason for the difficulties in fully reaching that target were the inherent differences between vector-borne diseases and “classical” infectious diseases, which make it necessary to specifically adjust the ontology’s architecture in order to comprise vectors and their populations.ConclusionsIn addition to a higher coverage of domain-specific terms and optimizing its usage by databases and decision-support systems, the new version of IDOMAL described here allows for more cross-talk between it and other ontologies, and in particular IDO. The malaria ontology is available for downloading at the OBO Foundry (http://www.obofoundry.org/cgi-bin/detail.cgi?id=malaria_ontology) and the NCBO BioPortal (http://bioportal.bioontology.org/ontologies/1311).

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“…The suite consists of the core IDO, covering terms and relations generally relevant to the infectious disease domain, and a set of domain-specific ontologies developed as extensions from the core [29]. To date, disease and pathogen specific extension ontologies have been developed for malaria [30], dengue fever [31], brucellosis[32], and Staphylococcus aureus [33, 34]. The primary purpose of the core IDO is to maximize interoperability between IDO extensions as well as with ontologies outside the IDO suite.…”

Section: Methodsmentioning

confidence: 99%

Combining expert knowledge and knowledge automatically acquired from electronic data sources for continued ontology evaluation and improvement

Gordon

Weng

2015

Journal of Biomedical Informatics

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Introduction A common bottleneck during ontology evaluation is knowledge acquisition from domain experts for gold standard creation. This paper contributes a novel semi-automated method for evaluating the concept coverage and accuracy of biomedical ontologies by complementing expert knowledge with knowledge automatically extracted from clinical practice guidelines and electronic health records, which minimizes reliance on expensive domain expertise for gold standards generation. Methods We developed a bacterial clinical infectious diseases ontology (BCIDO) to assist clinical infectious disease treatment decision support. Using a semi-automated method we integrated diverse knowledge sources, including publically available infectious disease guidelines from international repositories, electronic health records, and expert-generated infectious disease case scenarios, to generate a compendium of infectious disease knowledge and use it to evaluate the accuracy and coverage of BCIDO. Results BCIDO has three classes (i.e., infectious disease, antibiotic, bacteria) containing 593 distinct concepts and 2345 distinct concept relationships. Our semi-automated method generated an ID knowledge compendium consisting of 637 concepts and 1554 concept relationships. Overall, BCIDO covered 79% (504/637) of the concepts and 89% (1378/1554) of the concept relationships in the ID compendium. BCIDO coverage of ID compendium concepts was 92% (121/131) for antibiotic, 80% (205/257) for infectious disease, and 72% (178/249) for bacteria. The low coverage of bacterial concepts in BCIDO was due to a difference in concept granularity between BCIDO and infectious disease guidelines. Guidelines and expert generated scenarios were the richest source of ID concpets and relationships while patient records provided relatively fewer concepts and relationships. Conclusions Our semi-automated method was cost-effective for generating a useful knowledge compendium with minimal reliance on domain experts. This method can be useful for continued development and evaluation of biomedical ontologies for better accuracy and coverage.

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Towards an ontological representation of resistance: The case of MRSA

Cited by 14 publications

References 8 publications

Ontological realism: A methodology for coordinated evolution of scientific ontologies

Ontological realism: A methodology for coordinated evolution of scientific ontologies

IDOMAL: the malaria ontology revisited

Combining expert knowledge and knowledge automatically acquired from electronic data sources for continued ontology evaluation and improvement

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