The Physiome Project,
            <i>open</i>
            EHR Archetypes, and the Digital Patient

Nickerson, David; Atalag, Koray; Bono, Bernard de; Hunter, Peter

doi:10.1002/9781118952788.ch9

Cited by 10 publications

(6 citation statements)

References 32 publications

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“…It will similarly facilitate parameter modification from tools and platforms that use clinical information to constrain and contextualise mathematical models (Nickerson et al . ).…”

Section: Introductionmentioning

confidence: 97%

Modular modelling with Physiome standards

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Nickerson

Nielsen

et al. 2016

The Journal of Physiology

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The ability to produce and customise complex computational models has great potential to have a positive impact on human health. As the field develops towards whole-cell models and linking such models in multi-scale frameworks to encompass tissue, organ, or organism levels, reuse of previous modelling efforts will become increasingly necessary. Any modelling group wishing to reuse existing computational models as modules for their own work faces many challenges in the context of construction, storage, retrieval, documentation and analysis of such modules. Physiome standards, frameworks and tools seek to address several of these challenges, especially for models expressed in the modular protocol CellML. Aside from providing a general ability to produce modules, there has been relatively little research work on architectural principles of CellML models that will enable reuse at larger scales. To complement and support the existing tools and frameworks, we develop a set of principles to address this consideration. The principles are illustrated with examples that couple electrophysiology, signalling, metabolism, gene regulation and synthetic biology, together forming an architectural prototype for whole-cell modelling (including human intervention) in CellML. Such models illustrate how testable units of quantitative biophysical simulation can be constructed. Finally, future relationships between modular models so constructed and Physiome frameworks and tools are discussed, with particular reference to how such frameworks and tools can in turn be extended to complement and gain more benefit from the results of applying the principles.

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“…It will similarly facilitate parameter modification from tools and platforms that use clinical information to constrain and contextualise mathematical models (Nickerson et al . ).…”

Section: Introductionmentioning

confidence: 97%

Modular modelling with Physiome standards

Cooling

Nickerson

Nielsen

et al. 2016

The Journal of Physiology

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“…Computational tools and standards [17, 18] have evolved over the years to store, exchange and comprehend computational models, including their comprehensive descriptions, i.e. semantic annotation, which make these resources more accessible and discoverable.…”

Section: Introductionmentioning

confidence: 99%

Model annotation and discovery with the Physiome Model Repository

et al. 2019

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Background Mathematics and Phy sics-based simulation models have the potential to help interpret and encapsulate biological phenomena in a computable and reproducible form. Similarly, comprehensive descriptions of such models help to ensure that such models are accessible, discoverable, and reusable. To this end, researchers have developed tools and standards to encode mathematical models of biological systems enabling reproducibility and reuse, tools and guidelines to facilitate semantic description of mathematical models, and repositories in which to archive, share, and discover models. Scientists can leverage these resources to investigate specific questions and hypotheses in a more efficient manner. Results We have comprehensively annotated a cohort of models with biological semantics. These annotated models are freely available in the Physiome Model Repository (PMR). To demonstrate the benefits of this approach, we have developed a web-based tool which enables users to discover models relevant to their work, with a particular focus on epithelial transport. Based on a semantic query, this tool will help users discover relevant models, suggesting similar or alternative models that the user may wish to explore or use. Conclusion The semantic annotation and the web tool we have developed is a new contribution enabling scientists to discover relevant models in the PMR as candidates for reuse in their own scientific endeavours. This approach demonstrates how semantic web technologies and methodologies can contribute to biomedical and clinical research. The source code and links to the web tool are available at https://github.com/dewancse/model-discovery-tool

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“…Semantic similarity between annotated biomedical resources has been a topic of research since Lord et al [1] applied this technique to annotated proteins, as a search tool within a protein database. With the increase in the amount of biomedical domains being represented in formal ontologies, the desire to use ontologies to annotate biomedical entities increases, which resulted in multiple ontologies being used to that effect: metabolic pathways [2, 3], mathematical models of biological processes [4], functional tissue units [5], epidemiological resources [6], biomedical text and clinical notes [7], chemical toxicity [8] etc . These multidisciplinary entities, along with their multi-ontology annotations, can be regarded as biomedical digital resources that describe complex real-world phenomena.…”

Section: Introductionmentioning

confidence: 99%

Multi-domain semantic similarity in biomedical research

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Couto

2019

BMC Bioinformatics

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Background Given the increasing amount of biomedical resources that are being annotated with concepts from more than one ontology and covering multiple domains of knowledge, it is important to devise mechanisms to compare these resources that take into account the various domains of annotation. For example, metabolic pathways are annotated with their enzymes and their metabolites, and thus similarity measures should compare them with respect to both of those domains simultaneously. Results In this paper, we propose two approaches to lift existing single-ontology semantic similarity measures into multi-domain measures. The aggregative approach compares domains independently and averages the various similarity values into a final score. The integrative approach integrates all the relevant ontologies into a single one, calculating similarity in the resulting multi-domain ontology using the single-ontology measure. Conclusions We evaluated the two approaches in a multidisciplinary epidemiology dataset by evaluating the capacity of the similarity measures to predict new annotations based on the existing ones. The results show a promising increase in performance of the multi-domain measures over the single-ontology ones in the vast majority of the cases. These results show that multi-domain measures outperform single-domain ones, and should be considered by the community as a starting point to study more efficient multi-domain semantic similarity measures.

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The Physiome Project, open EHR Archetypes, and the Digital Patient

Cited by 10 publications

References 32 publications

Modular modelling with Physiome standards

Modular modelling with Physiome standards

Model annotation and discovery with the Physiome Model Repository

Multi-domain semantic similarity in biomedical research

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