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.

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.

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.

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.

Neuroanatomical domain of the foundational model of anatomy ontology.

Background: The diverse set of human brain structure and function analysis methods represents a difficult challenge for reconciling multiple views of neuroanatomical organization. While different views of organization are expected and valid, no widely adopted approach exists to harmonize different brain labeling protocols and terminologies. Our approach uses the natural organizing framework provided by anatomical structure to correlate terminologies commonly used in neuroimaging.

Towards understanding craniofacial abnormalities: the ontology of craniofacial development and malformation.

We introduce the Ontology of Craniofacial Development and Malformation (OCDM), a project of the NIH-funded FaceBase consortium, whose goal is to gather data from multiple species, at levels ranging from genes to gross anatomy, in order to understand the causes of craniofacial abnormalities. The OCDM is being developed in order to facilitate integration of these diverse forms of data in a central Hub. It currently consists of several components, including human adult and developmental anatomy, corresponding mouse structures, and malformations.

Human Development Domain of the Ontology of Craniofacial Development and Malformation.

In this paper we describe an ontological scheme for representing anatomical entities undergoing morphological transformation and changes in phenotype during prenatal development. This is a proposed component of the Anatomical Transformation Abstraction (ATA) of the Foundational Model of Anatomy (FMA) Ontology that was created to provide an ontological framework for capturing knowledge about human development from the zygote to postnatal life. It is designed to initially describe the structural properties of the anatomical entities that participate in human development and then enhance their description with developmental properties, such as temporal attributes and developmental processes.

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.

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.

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.

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.

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.

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.

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.

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.

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.

Challenges in reconciling different views of neuroanatomy in a reference ontology of anatomy.

A fundamental requirement for integrating neuroscience data is a well-structured ontology that can incorporate, accommodate and reconcile different neuroanatomical views. Here we describe the challenges in creating such ontology, and, because of its principled design, illustrate the potential of the Foundational Model of Anatomy to be that ontology.

A strategy for improving and integrating biomedical ontologies.

The integration of biomedical terminologies is indispensable to the process of information integration. When terminologies are linked merely through the alignment of their leaf terms, however, differences in context and ontological structure are ignored. Making use of the SNAP and SPAN ontologies, we show how three reference domain ontologies can be integrated at a higher level, through what we shall call the OBR framework (for: Ontology of Biomedical Reality).

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.

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.

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.

A reference ontology for biomedical informatics: the Foundational Model of Anatomy.

The Foundational Model of Anatomy (FMA), initially developed as an enhancement of the anatomical content of UMLS, is a domain ontology of the concepts and relationships that pertain to the structural organization of the human body. It encompasses the material objects from the molecular to the macroscopic levels that constitute the body and associates with them non-material entities (spaces, surfaces, lines, and points) required for describing structural relationships. The disciplined modeling approach employed for the development of the FMA relies on a set of declared principles, high level schemes, Aristotelian definitions and a frame-based authoring environment.

The evolving neuroanatomical component of the Foundational Model of Anatomy.

In order to meet the need for an expressive ontology in neuroinformatics, we have integrated the extensive terminologies of NeuroNames and Terminologia Anatomica into the Foundational Model of Anatomy (FMA). We have enhanced the FMA to accommodate information unique to neuronal structures, such as axonal input/output relationships.

Proposed classification of cells in the Foundational Model of Anatomy.

A logical and principled representation of cell types and their component parts could serve as a framework for correlating the various ontologies that are emerging in bioinformatics with a focus on cells and subcellular biological entities. In order to address this need we have extended the Foundational Model of Anatomy (FMA)1,2 from macroscopic to cellular and subcellular anatomical entities. The poster will provide a live demonstration of this implementation.