Epithelial Modelling Platform: A Tool for Model Discovery and Assembly with the Physiome Model Repository

Sarwar, Dewan M.; Munarko, Yuda; Nickerson, David

doi:10.1101/631465

Cited by 1 publication

(3 citation statements)

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“…Currently, we develop NLIMED over CellML in the PMR (Yu et al, 2011) and implement it in EMP (Sarwar et al, 2019) as an additional interface discovering model entities and being able to meet EMP requirements. We are confident that NLIMED may also be developed over BioModels (Chelliah et al, 2015) since it contains models richly annotated with the RDF utilising class ontologies.…”

Section: Possible Implementationmentioning

confidence: 99%

“…This approach works well, but by using NCBO Annotator only we do not consider local data in biosimulation models that are likely to cause misidentification. Another tool, Epithelial Modelling Platform (EMP), a visual web interface to create a new epithelial transport model based on the available models (Sarwar et al, 2019), utilised a template-based search interface (Sarwar et al, 2018) to find the appropriate model entities. For this specific type of searching, the template-based interface can perform properly and is easy to deploy; however, for our purpose, it needs the development of numerous templates for different biosimulation model types.…”

Section: Introductionmentioning

confidence: 99%

“…NLIMED is ready to be implemented and can accommodate different biosimulation model platforms, such as CellML (Cuellar et al, 2003), and SBML (Hucka et al, 2003) so it is possible to create generic model entities discovery tool. Currently, we have implemented NLIMED in EMP to optimise its user experience when searching for model entities (Sarwar et al, 2019). We provide our implementation and experiment setup freely accessed at https://github.com/napakalas/NLIMED.…”

Section: Introductionmentioning

confidence: 99%

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NLIMED: Natural Language Interface for Model Entity Discovery in Biosimulation Model Repositories

Munarko

Atalag

et al. 2019

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Motivation: Semantic annotation is a crucial step to assure reusability and reproducibility of biosimulation models in biology and physiology. For this purpose, the COmputational Modeling in BIology NEtwork (COMBINE) community recommend the use of the Resource Description Framework (RDF). The RDF implementation provides the flexibility of model entity searching (e.g. flux of sodium across apical plasma membrane) by utilising SPARQL. However, the rigidity and complexity of SPARQL syntax and the nature of semantic annotation which is not merely as a simple triple yet forming a tree-like structure may cause a difficulty. Therefore, the availability of an interface to convert a natural language query to SPARQL is beneficial. Results: We propose NLIMED, a natural language query to SPARQL interface to retrieve model entities from biosimulation models. Our interface can be applied to various repositories utilising RDF such as the PMR and Biomodels. We evaluate our interface by collecting RDF in the biosimulation models coded using CellML in PMR. First, we extract RDF as a tree structure and then store each subtree of a model entity as a modified triple of a model entity name, path, and class ontology into the RDF Graph Index. We also extract class ontology's textual metadata from the BioPortal and CellML and manage it in the Text Feature Index. With the Text Feature Index, we annotate phrases resulted by the NLQ Parser (Stanford parser or NLTK parser) into class ontologies. Finally, the detected class ontologies then are composed as SPARQL by incorporating the RDF Graph Index. Our annotator performance is far more powerful compared to the available service provided by BioPortal with F-measure of 0.756 and our SPARQL composer can find all possible SPARQL in the collection based on the annotation results. Currently, we already implement our interface in Epithelial Modelling Platform tool. Availability: https://github.com/napakalas/NLIMED Contact: ymun794@aucklanduni.ac.nz, d.nickerson@auckland.ac.nz

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Section: Possible Implementationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%