Evaluating the granularity balance of hierarchical relationships within large biomedical terminologies towards quality improvement.

Organizing the descendants of a concept under a particular semantic relationship may be rather arbitrarily carried out during the manual creation processes of large biomedical terminologies, resulting in imbalances in relationship granularity. This work aims to propose scalable models towards systematically evaluating the granularity balance of semantic relationships. We first utilize "parallel concepts set (PCS)" and two features (the length and the strength) of the paths between PCSs to design the general evaluation models, based on which we propose eight concrete evaluation models generated by two specific types of PCSs: single concept set and symmetric concepts set. We then apply those concrete models to the IS-A relationship in FMA and SNOMED CT's Body Structure subset, as well as to the Part-Of relationship in FMA. Moreover, without loss of generality, we conduct two additional rounds of applications on the Part-Of relationship after removing length redundancies and strength redundancies sequentially. At last, we perform automatic evaluation on the imbalances detected after the final round for identifying missing concepts, misaligned relations and inconsistencies. For the IS-A relationship, 34 missing concepts, 80 misalignments and 18 redundancies in FMA as well as 28 missing concepts, 114 misalignments and 1 redundancy in SNOMED CT were uncovered. In addition, 6,801 instances of imbalances for the Part-Of relationship in FMA were also identified, including 3,246 redundancies. After removing those redundancies from FMA, the total number of Part-Of imbalances was dramatically reduced to 327, including 51 missing concepts, 294 misaligned relations, and 36 inconsistencies. Manual curation performed by the FMA project leader confirmed the effectiveness of our method in identifying curation errors. In conclusion, the granularity balance of hierarchical semantic relationship is a valuable property to check for ontology quality assurance, and the scalable evaluation models proposed in this study are effective in fulfilling this task, especially in auditing relationships with sub-hierarchies, such as the seldom evaluated Part-Of relationship.

Ontology-based image navigation: exploring 3.0-T MR neurography of the brachial plexus using AIM and RadLex.

Disorders of the peripheral nervous system have traditionally been evaluated using clinical history, physical examination, and electrodiagnostic testing. In selected cases, imaging modalities such as magnetic resonance (MR) neurography may help further localize or characterize abnormalities associated with peripheral neuropathies, and the clinical importance of such techniques is increasing. However, MR image interpretation with respect to peripheral nerve anatomy and disease often presents a diagnostic challenge because the relevant knowledge base remains relatively specialized. Using the radiology knowledge resource RadLex®, a series of RadLex queries, the Annotation and Image Markup standard for image annotation, and a Web services-based software architecture, the authors developed an application that allows ontology-assisted image navigation. The application provides an image browsing interface, allowing users to visually inspect the imaging appearance of anatomic structures. By interacting directly with the images, users can access additional structure-related information that is derived from RadLex (eg, muscle innervation, muscle attachment sites). These data also serve as conceptual links to navigate from one portion of the imaging atlas to another. With 3.0-T MR neurography of the brachial plexus as the initial area of interest, the resulting application provides support to radiologists in the image interpretation process by allowing efficient exploration of the MR imaging appearance of relevant nerve segments, muscles, bone structures, vascular landmarks, anatomic spaces, and entrapment sites, and the investigation of neuromuscular relationships.

An analysis of FMA using structural self-bisimilarity.

As ontologies are mostly manually created, they tend to contain errors and inconsistencies. In this paper, we present an automated computational method to audit symmetric concepts in ontologies by leveraging self-bisimilarity and linguistic structure in the concept names. Two concepts A and B are symmetric if concept B can be obtained from concept A by replacing a single modifier such as "left" with its symmetric modifier such as "right." All possible local structural types for symmetric concept pairs are enumerated according to their local subsumption hierarchy, and the pairs are further classified into Non-Matches and Matches. To test the feasibility and validate the benefits of this method, we computed all the symmetric modifier pairs in the Foundational Model of Anatomy (FMA) and selected six of them for experimentation. 9893 Non-Matches and 221 abnormal Matches with potential errors were discovered by our algorithm. Manual evaluation by FMA domain experts on 176 selected Non-Matches and all the 221 abnormal Matches found 102 missing concepts and 40 misaligned concepts. Corrections for them have currently been implemented in the latest version of FMA. Our result demonstrates that self-bisimilarity can be a valuable method for ontology quality assurance, particularly in uncovering missing concepts and misaligned concepts. Our approach is computationally scalable and can be applied to other ontologies that are rich in symmetric concepts.

Ontological labels for automated location of anatomical shape differences.

A method for automated location of shape differences in diseased anatomical structures via high resolution biomedical atlases annotated with labels from formal ontologies is described. In particular, a high resolution magnetic resonance image of the myocardium of the human left ventricle was segmented and annotated with structural terms from an extracted subset of the Foundational Model of Anatomy ontology. The atlas was registered to the end systole template of a previous study of left ventricular remodeling in cardiomyopathy using a diffeomorphic registration algorithm. The previous study used thresholding and visual inspection to locate a region of statistical significance which distinguished patients with ischemic cardiomyopathy from those with nonischemic cardiomyopathy. Using semantic technologies and the deformed annotated atlas, this location was more precisely found. Although this study used only a cardiac atlas, it provides a proof-of-concept that ontologically labeled biomedical atlases of any anatomical structure can be used to automate location-based inferences.

Content-specific auditing of a large scale anatomy ontology.

Biomedical ontologies are envisioned to be usable in a range of research and clinical applications. The requirements for such uses include formal consistency, adequacy of coverage, and possibly other domain specific constraints. In this report we describe a case study that illustrates how application specific requirements may be used to identify modeling problems as well as data entry errors in ontology building and evolution. We have begun a project to use the UW Foundational Model of Anatomy (FMA) in a clinical application in radiation therapy planning. This application focuses mainly (but not exclusively) on the representation of the lymphatic system in the FMA, in order to predict the spread of tumor cells to regional metastatic sites. This application requires that the downstream relations associated with lymphatic system components must only be to other lymphatic chains or vessels, must be at the appropriate level of granularity, and that every path through the lymphatic system must terminate at one of the two well known trunks of the lymphatic system. It is possible through a programmable query interface to the FMA to write small programs that systematically audit the FMA for compliance with these constraints. We report on the design of some of these programs, and the results we obtained by applying them to the lymphatic system. The algorithms and approach are generalizable to other network organ systems in the FMA such as arteries and veins. In addition to illustrating exact constraint checking methods, this work illustrates how the details of an application may reflect back a requirement to revise the design of the ontology itself.

Relationship auditing of the FMA ontology.

The Foundational Model of Anatomy (FMA) ontology is a domain reference ontology based on a disciplined modeling approach. Due to its large size, semantic complexity and manual data entry process, errors and inconsistencies are unavoidable and might remain within the FMA structure without detection. In this paper, we present computable methods to highlight candidate concepts for various relationship assignment errors. The process starts with locating structures formed by transitive structural relationships (part_of, tributary_of, branch_of) and examine their assignments in the context of the IS-A hierarchy. The algorithms were designed to detect five major categories of possible incorrect relationship assignments: circular, mutually exclusive, redundant, inconsistent, and missed entries. A domain expert reviewed samples of these presumptive errors to confirm the findings. Seven thousand and fifty-two presumptive errors were detected, the largest proportion related to part_of relationship assignments. The results highlight the fact that errors are unavoidable in complex ontologies and that well designed algorithms can help domain experts to focus on concepts with high likelihood of errors and maximize their effort to ensure consistency and reliability. In the future similar methods might be integrated with data entry processes to offer real-time error detection.

From SNOMED CT to Uberon: Transferability of evaluation methodology between similarly structured ontologies.

OBJECTIVE: To examine whether disjoint partial-area taxonomy, a semantically-based evaluation methodology that has been successfully tested in SNOMED CT, will perform with similar effectiveness on Uberon, an anatomical ontology that belongs to a structurally similar family of ontologies as SNOMED CT. METHOD: A disjoint partial-area taxonomy was generated for Uberon. One hundred randomly selected test concepts that overlap between partial-areas were matched to a same size control sample of non-overlapping concepts. The samples were blindly inspected for non-critical issues and presumptive errors first by a general domain expert whose results were then confirmed or rejected by a highly experienced anatomical ontology domain expert. Reported issues were subsequently reviewed by Uberon's curators. RESULTS: Overlapping concepts in Uberon's disjoint partial-area taxonomy exhibited a significantly higher rate of all issues. Clear-cut presumptive errors trended similarly but did not reach statistical significance. A sub-analysis of overlapping concepts with three or more relationship types indicated a much higher rate of issues. CONCLUSIONS: Overlapping concepts from Uberon's disjoint abstraction network are quite likely (up to 28.9%) to exhibit issues. The results suggest that the methodology can transfer well between same family ontologies. Although Uberon exhibited relatively few overlapping concepts, the methodology can be combined with other semantic indicators to expand the process to other concepts within the ontology that will generate high yields of discovered issues.

From frames to OWL2: Converting the Foundational Model of Anatomy.

OBJECTIVE: The Foundational Model of Anatomy (FMA) [Rosse C, Mejino JLV. A reference ontology for bioinformatics: the Foundational Model of Anatomy. J. Biomed. Inform. 2003;36:478-500] is an ontology that represents canonical anatomy at levels ranging from the entire body to biological macromolecules, and has 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 information, and because of the importance of the FMA in particular in representing human anatomy, the FMA in OWL should greatly accelerate the development of an anatomically based structural information framework for organizing and linking a large amount of biomedical information.

Multi-species Ontologies of the Craniofacial Musculoskeletal System.

We created the Ontology of Craniofacial Development and Malformation (OCDM) [1] to provide a unifying framework for organizing and integrating craniofacial data ranging from genes to clinical phenotypes from multi-species. Within this framework we focused on spatio-structural representation of anatomical entities related to craniofacial development and malformation, such as craniosynostosis and midface hypoplasia. Animal models are used to support human studies and so we built multi-species ontologies that would allow for cross-species correlation of anatomical information. For this purpose we first developed and enhanced the craniofacial component of the human musculoskeletal system in the Foundational Model of Anatomy Ontology (FMA)[2], and then imported this component, which we call the Craniofacial Human Ontology (CHO), into the OCDM. The CHO was then used as a template to create the anatomy for the mouse, the Craniofacial Mouse Ontology (CMO) as well as for the zebrafish, the Craniofacial Zebrafish Ontology (CZO).

Development of a Novel Markov Chain Model for the Prediction of Head and Neck Squamous Cell Carcinoma Dissemination.

Prediction of microscopic tumor spread to regional lymph nodes can assist in radiation planning for cancer treatment. However, it is still challenging to predict tumor spread. In this paper, we present a unique approach to modeling how tumor cells disseminate to form regional metastases. This involves leveraging well established knowledge resources and commonly held notions of how cancer spreads. Using patient data, we utilized our approach to create a model of metastasis for the subset of head and neck squamous cell carcinoma that arises in the mucosa of the lateral tongue. The model was created using a training set extracted from the clinical records of 50 patients with tumors of this type who presented to the University of Washington head and tumor board over a three and half year period. The test sets consist of four case series drawn from the literature.

Processes and problems in the formative evaluation of an interface to the Foundational Model of Anatomy knowledge base.

The Digital Anatomist Foundational Model of Anatomy (FMA) is a large semantic network of more than 100,000 terms that refer to the anatomical entities, which together with 1.6 million structural relationships symbolically represent the physical organization of the human body. Evaluation of such a large knowledge base by domain experts is challenging because of the sheer size of the resource and the need to evaluate not just classes but also relationships. To meet this challenge, the authors have developed a relation-centric query interface, called Emily, that is able to query the entire range of classes and relationships in the FMA, yet is simple to use by a domain expert. Formative evaluation of this interface considered the ability of Emily to formulate queries based on standard anatomy examination questions, as well as the processing speed of the query engine. Results show that Emily is able to express 90% of the examination questions submitted to it and that processing time is generally 1 second or less, but can be much longer for complex queries. These results suggest that Emily will be a very useful tool, not only for evaluating the FMA, but also for querying and evaluating other large semantic networks.

Evolution of a Foundational Model of Physiology: symbolic representation for functional bioinformatics.

We describe the need for a Foundational Model of Physiology (FMP) as a reference ontology for "functional bioinformatics". The FMP is intended to support symbolic lookup, logical inference and mathematical analysis by integrating descriptive, qualitative and quantitative functional knowledge. The FMP will serve as a symbolic representation of biological functions initially pertaining to human physiology and ultimately extensible to other species. We describe the evolving architecture of the FMP, which is based on the ontological principles of the BioD biological description language and the Foundational Model of Anatomy (FMA).

Problems and solutions with integrating terminologies into evolving knowledge bases.

We have merged two established anatomical terminologies with an evolving ontology of biological structure: the Foundational Model of Anatomy. We describe the problems we have encountered and the solutions we have developed. We believe that both the problems and solutions generalize to the integration of any legacy terminology with a disciplined ontology within the same domain.

A relation-centric query engine for the Foundational Model of Anatomy.

The Foundational Model of Anatomy (FMA), a detailed representation of the structural organization of the human body, was constructed to support the development of software applications requiring knowledge of anatomy. The FMA's focus on the structural relationships between anatomical entities distinguishes it from other current anatomical knowledge sources. We developed Emily, a query engine for the FMA, to enable users to explore the richness and depth of these relationships. Preliminary analysis suggests that Emily is capable of correctly processing real world anatomical queries provided they have been translated into a constrained form suitable for processing by the query engine.

Evaluation and integration of disparate classification systems for clefts of the lip.

Orofacial clefting is a common birth defect with wide phenotypic variability. Many systems have been developed to classify cleft patterns to facilitate diagnosis, management, surgical treatment, and research. In this review, we examine the rationale for different existing classification schemes and determine their inter-relationships, as well as strengths and deficiencies for subclassification of clefts of the lip. The various systems differ in how they describe and define attributes of cleft lip (CL) phenotypes. Application and analysis of the CL classifications reveal discrepancies that may result in errors when comparing studies that use different systems. These inconsistencies in terminology, variable levels of subclassification, and ambiguity in some descriptions may confound analyses and impede further research aimed at understanding the genetics and etiology of clefts, development of effective treatment options for patients, as well as cross-institutional comparisons of outcome measures. Identification and reconciliation of discrepancies among existing systems is the first step toward creating a common standard to allow for a more explicit interpretation that will ultimately lead to a better understanding of the causes and manifestations of phenotypic variations in clefting.

Application of neuroanatomical ontologies for neuroimaging data annotation.

The annotation of functional neuroimaging results for data sharing and re-use is particularly challenging, due to the diversity of terminologies of neuroanatomical structures and cortical parcellation schemes. To address this challenge, we extended the Foundational Model of Anatomy Ontology (FMA) to include cytoarchitectural, Brodmann area labels, and a morphological cortical labeling scheme (e.g., the part of Brodmann area 6 in the left precentral gyrus). This representation was also used to augment the neuroanatomical axis of RadLex, the ontology for clinical imaging. The resulting neuroanatomical ontology contains explicit relationships indicating which brain regions are "part of" which other regions, across cytoarchitectural and morphological labeling schemas. We annotated a large functional neuroimaging dataset with terms from the ontology and applied a reasoning engine to analyze this dataset in conjunction with the ontology, and achieved successful inferences from the most specific level (e.g., how many subjects showed activation in a subpart of the middle frontal gyrus) to more general (how many activations were found in areas connected via a known white matter tract?). In summary, we have produced a neuroanatomical ontology that harmonizes several different terminologies of neuroanatomical structures and cortical parcellation schemes. This neuroanatomical ontology is publicly available as a view of FMA at the Bioportal website. The ontological encoding of anatomic knowledge can be exploited by computer reasoning engines to make inferences about neuroanatomical relationships described in imaging datasets using different terminologies. This approach could ultimately enable knowledge discovery from large, distributed fMRI studies or medical record mining.

Composite annotations: requirements for mapping multiscale data and models to biomedical ontologies.

Current methods for annotating biomedical data resources rely on simple mappings between data elements and the contents of a variety of biomedical ontologies and controlled vocabularies. Here we point out that such simple mappings are inadequate for large-scale multiscale, multidomain integrative "virtual human" projects. For such integrative challenges, we describe a "composite annotation" schema that is simple yet sufficiently extensible for mapping the biomedical content of a variety of data sources and biosimulation models to available biomedical ontologies.

FMA-RadLex: An application ontology of radiological anatomy derived from the foundational model of anatomy reference ontology.

Domain reference ontologies are being developed to serve as generalizable and reusable sources designed to support any application specific to the domain. The challenge is how to develop ways to derive or adapt pertinent portions of reference ontologies into application ontologies. In this paper we demonstrate how a subset of anatomy relevant to the domain of radiology can be derived from an anatomy reference ontology, the Foundational Model of Anatomy (FMA) Ontology, to create an application ontology that is robust and expressive enough to incorporate and accommodate all salient anatomical knowledge necessary to support existing and emerging systems for managing anatomical information related to radiology. The principles underlying this work are applicable to domains beyond radiology, so our results could be extended to other areas of biomedicine in the future.

Web service access to semantic web ontologies for data annotation.

Recent advances in semantic web technologies now provide the methodology for efficient and adaptable deployment of ontology support to biomedical applications for data annotation and integration.

Bridging biological ontologies and biosimulation: the ontology of physics for biology.

We introduce and define the Ontology of Physics for Biology (OPB), a reference ontology of physical principles that bridges the gap between bioinformat-ics modeling of biological structures and the bio-simulation modeling of biological processes. Where-as modeling anatomical entities is relatively well-studied, representing the physics-based semantics of biosimulation and biological processes remains an open research challenge. The OPB bridges this semantic gap-linking the semantics of biosimulation mathematics to structural bio-ontologies. Our design of the OPB is driven both by theory and pragmatics: we have applied systems dynamics theory to build an ontology with pragmatic use for annotating biosimulation models.