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  Ontology patterns for the representation of quality changes of cells in time

Burek, P., Scherf, N., & Herre, H. (2019). Ontology patterns for the representation of quality changes of cells in time. Journal of Biomedical Semantics, 10: 16. doi:10.1186/s13326-019-0206-4.

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Item Permalink: http://hdl.handle.net/21.11116/0000-0004-C690-9 Version Permalink: http://hdl.handle.net/21.11116/0000-0004-F626-C
Genre: Journal Article

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 Creators:
Burek, Patryk1, Author
Scherf, Nico2, Author              
Herre, Heinrich3, Author
Affiliations:
1Institute of Computer Science, Faculty of Mathematics, Physics and Computer Science, Marii Curie-Sklodowskiej University, pl. Marii Curie-Sklodowskiej 5, 20-031 Lublin, Poland, ou_persistent22              
2Department Neurophysics (Weiskopf), MPI for Human Cognitive and Brain Sciences, Max Planck Society, ou_2205649              
3Institute for Medical Informatics, Statistics and Epidemiology, University of Leipzig, Germany, ou_persistent22              

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 Abstract: Background: Cell tracking experiments, based on time-lapse microscopy, have become an important tool in biomedical research. The goal is the reconstruction of cell migration patterns, shape and state changes, and, comprehensive genealogical information from these data. This information can be used to develop process models of cellular dynamics. However, so far there has been no structured, standardized way of annotating and storing the tracking results, which is critical for comparative analysis and data integration. The key requirement to be satisfied by an ontology is the representation of a cell’s change over time. Unfortunately, popular ontology languages, such as Web Ontology Language (OWL), have limitations for the representation of temporal information. The current paper addresses the fundamental problem of modeling changes of qualities over time in biomedical ontologies specified in OWL. Results: The presented analysis is a result of the lessons learned during the development of an ontology, intended for the annotation of cell tracking experiments. We present, discuss and evaluate various representation patterns for specifying cell changes in time. In particular, we discuss two patterns of temporally changing information: n-ary relation reification and 4d fluents.These representation schemes are formalized within the ontology language OWL and are aimed at the support for annotation of cell tracking experiments. We analyze the performance of each pattern with respect to standard criteria used in software engineering and data modeling, i.e. simplicity, scalability, extensibility and adequacy. We further discuss benefits, drawbacks, and the underlying design choices of each approach. Conclusions: We demonstrate that patterns perform differently depending on the temporal distribution of modeled information. The optimal model can be constructed by combining two competitive approaches. Thus, we demonstrate that both reification and 4d fluents patterns can work hand in hand in a single ontology. Additionally, we have found that 4d fluents can be reconstructed by two patterns well known in the computer science community, i.e. state modeling and actor-role pattern.

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Language(s): eng - English
 Dates: 2018-07-212019-07-312019-10-162019-12
 Publication Status: Published in print
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 Rev. Method: Peer
 Identifiers: DOI: 10.1186/s13326-019-0206-4
PMID: 31619282
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Funding organization : German Research Foundation (DFG)
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Funding program : Open Access Publishing
Funding organization : University of Leipzig

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Title: Journal of Biomedical Semantics
Source Genre: Journal
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Publ. Info: London : BioMed Central
Pages: - Volume / Issue: 10 Sequence Number: 16 Start / End Page: - Identifier: ISSN: 2041-1480
CoNE: https://pure.mpg.de/cone/journals/resource/2041-1480