The Foundational Model of Anatomy in OWL 2 and its use

Golbreich, Christine; Grosjean, Julien; Darmoni, Stéfan Jacques

doi:10.1016/j.artmed.2012.11.002

Cited by 28 publications

(28 citation statements)

References 6 publications

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“…In particular it is based on that of Golbreich, et al (7-9), but was also informed by those of Noy, et al (10) and Dameron, et al (11) as well as unpublished investigations by Alan Ruttenberg and by Robert Hoehndorf.…”

Section: Conversion Proceduresmentioning

confidence: 93%

“…The OWL API is used to generate the OWL classes, individuals, properties, axioms, etc., and to write the resulting model out to a file. Use of the APIs is an example of one of the changes that we wanted to make to the prior conversion tool most similar to our own, but for which we were unable to obtain the source code (7). That conversion required that the FMA, originally stored in a Protégé Frames database backend, first be converted into the Protégé flat file format CLIPS.…”

Section: Conversion Proceduresmentioning

confidence: 99%

“…The conversion of the FMA into OWL has been attempted several times in the past (7-11). Our original intension was to extend the implementations from one or more of these earlier efforts.…”

Section: Introductionmentioning

confidence: 99%

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From frames to OWL2: Converting the Foundational Model of Anatomy

Detwiler

Mejino

Brinkley

2016

Artificial Intelligence in Medicine

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Objective The Foundational Model of Anatomy (FMA) is an ontology that represents canonical anatomy at levels ranging from the entire body to biological macromolecules, and is rapidly become the primary reference ontology for human anatomy, and a template for model organisms. Prior to this work, the FMA was developed in a knowledge modeling language known as Protégé Frames. Frames is an intuitive representational language, but is no longer the industry standard. Recognizing the need for an official version of the FMA in the more modern semantic web language OWL2 (hereafter referred to as OWL), the objective of this work was to create a generalizable Frames-to-OWL conversion tool, to use the tool to convert the FMA to OWL, to “clean up” the converted FMA so that it classifies under an EL reasoner, and then to do all further development in OWL. Methods The conversion tool is a Java application that uses the Protégé knowledge representation API for interacting with the initial Frames ontology, and uses the OWL-API for producing new statements (axioms, etc.) in OWL. The converter is relation centric. The conversion is configurable, on a property-by-property basis, via user-specifiable XML configuration files. The best conversion, for each property, was determined in conjunction with the FMA knowledge author. The convertor is potentially generalizable, which we partially demonstrate by using it to convert our Ontology of Craniofacial Development and Malformation as well as the FMA. Post-conversion cleanup involved using the Explain feature of Protégé to trace classification errors under the ELK reasoner in Protégé, fixing the errors, then re-running the reasoner. Results We are currently doing all our development in the converted and cleaned-up version of the FMA. The FMA (updated every 3 months) is available via our FMA web page http://si.washington.edu/projects/fma, which also provides access to mailing lists, an issue tracker, a SPARQL endpoint (updated every week), and an online browser. The converted OCDM is available at http://www.si.washington.edu/projects/ocdm. The conversion code is open source, and available at http://purl.org/sig/software/frames2owl. Prior to the post-conversion cleanup 73% of the more than 100,000 classes were unsatisfiable. After correction of six types of errors no classes remained unsatisfiable. Conclusion Because our FMA conversion captures all or most of the information in the Frames version, is the only complete OWL version that classifies under an EL reasoner, and is maintained by the FMA authors themselves, we propose that this version should be the only official release version of the FMA in OWL, supplanting all other versions. Although several issues remain to be resolved post-conversion, release of a single, standardized version of the FMA in OWL will greatly facilitate its use in informatics research and in the development of a global knowledge base within the semantic web. Because of the fundamental nature of anatomy in both understanding and organizing biomedical infor...

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Section: Conversion Proceduresmentioning

confidence: 93%

Section: Conversion Proceduresmentioning

confidence: 99%

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From frames to OWL2: Converting the Foundational Model of Anatomy

Detwiler

Mejino

Brinkley

2016

Artificial Intelligence in Medicine

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“…In the eXframe default package, the organism, tissue type, cell type, disease state and chemical treatment taxonomies are mapped to NCBI Taxonomy (NCBITaxon) [34], FMA [12], CL [11], Disease Ontology (DO) [13] and Chemical Entities of Biological Interest Ontology (ChEBI) [35] terms, respectively. EFO [10], NCI Thesaurus [36] or Breda Tissue Ontology (BTO) [37] is also used to increase coverage when required.…”

Section: Methodsmentioning

confidence: 99%

“…In addition several ontologies and vocabularies have also been developed to describe biological specimens such as the organism, tissue, cell type, disease state. These include the Cell Ontology (CL) [11], the Foundation Model of Anatomy (FMA) [12], Disease Ontology (DO) [13] among numerous others.…”

Section: Introductionmentioning

confidence: 99%

Semantic Web repositories for genomics data using the eXframe platform

et al. 2014

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BackgroundWith the advent of inexpensive assay technologies, there has been an unprecedented growth in genomics data as well as the number of databases in which it is stored. In these databases, sample annotation using ontologies and controlled vocabularies is becoming more common. However, the annotation is rarely available as Linked Data, in a machine-readable format, or for standardized queries using SPARQL. This makes large-scale reuse, or integration with other knowledge bases very difficult.MethodsTo address this challenge, we have developed the second generation of our eXframe platform, a reusable framework for creating online repositories of genomics experiments. This second generation model now publishes Semantic Web data. To accomplish this, we created an experiment model that covers provenance, citations, external links, assays, biomaterials used in the experiment, and the data collected during the process. The elements of our model are mapped to classes and properties from various established biomedical ontologies. Resource Description Framework (RDF) data is automatically produced using these mappings and indexed in an RDF store with a built-in Sparql Protocol and RDF Query Language (SPARQL) endpoint.ConclusionsUsing the open-source eXframe software, institutions and laboratories can create Semantic Web repositories of their experiments, integrate it with heterogeneous resources and make it interoperable with the vast Semantic Web of biomedical knowledge.

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Advances in the development of PubCaseFinder, including the new application programming interface and matching algorithm

2022

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Over 10,000 rare genetic diseases have been identified, and millions of newborns are affected by severe rare genetic diseases each year. A variety of Human Phenotype Ontology (HPO)‐based clinical decision support systems (CDSS) and patient repositories have been developed to support clinicians in diagnosing patients with suspected rare genetic diseases. In September 2017, we released PubCaseFinder (https://pubcasefinder.dbcls.jp), a web‐based CDSS that provides ranked lists of genetic and rare diseases using HPO‐based phenotypic similarities, where top‐listed diseases represent the most likely differential diagnosis. We also developed a Matchmaker Exchange (MME) application programming interface (API) to query PubCaseFinder, which has been adopted by several patient repositories. In this paper, we describe notable updates regarding PubCaseFinder, the GeneYenta matching algorithm implemented in PubCaseFinder, and the PubCaseFinder API. The updated GeneYenta matching algorithm improves the performance of the CDSS automated differential diagnosis function. Moreover, the updated PubCaseFinder and new API empower patient repositories participating in MME and medical professionals to actively use HPO‐based resources.

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The Foundational Model of Anatomy in OWL 2 and its use

Cited by 28 publications

References 6 publications

From frames to OWL2: Converting the Foundational Model of Anatomy

From frames to OWL2: Converting the Foundational Model of Anatomy

Semantic Web repositories for genomics data using the eXframe platform

Advances in the development of PubCaseFinder, including the new application programming interface and matching algorithm

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