The INCF Digital Atlasing Program: Report on Digital Atlasing Standards in the Rodent Brain

The goal of the INCF Digital Atlasing Program is to provide the vision and direction necessary to make the rapidly growing collection of multidimensional data of the rodent brain (images, gene expression, etc.) widely accessible and usable to the international research community. This Digital Brain Atlasing Standards Task Force was formed in May 2008 to investigate the state of rodent brain digital atlasing, and formulate standards, guidelines, and policy recommendations.

Our first objective has been the preparation of a detailed document that includes the vision and specific description of an infrastructure, systems and methods capable of serving the scientific goals of the community, as well as practical issues for achieving
the goals. This report builds on the 1st INCF Workshop on Mouse and Rat Brain Digital Atlasing Systems (Boline et al., 2007, _Nature Preceedings_, doi:10.1038/npre.2007.1046.1) and includes a more detailed analysis of both the current state and desired state of digital atlasing along with specific recommendations for achieving these goals

Contrasting Knowledge Organization Systems for the Description of Research Products: the Case of Overlapping in the Agricultural Domain

AbstractThe use of Knowledge Organization Systems (KOS) as ontologies or terminologies for the description of scholarly contents requires a careful consideration of the domain and the KOS available. KOS in the same domain may differ in several dimensions including purpose, level of formality, structure and language. In consequence, curators of scientific data face the problem of selecting the relevant KOS, developing mappings when appropriate and deciding on their usage for annotating resources. In domains in which more than a KOS is available, curators need tools to help them in the decision making process. Due to the available heterogeneity of KOS, exploratory tools are required for an initial assessment of overlapping and differences. This paper reports on a practical experience using simple mapping analysis and mapping visualizations in the domain of agriculture. These techniques represent promising directions for the development of decision tools based on the contrast of different KOS metrics

Designing novel abstraction networks for ontology summarization and quality assurance

Biomedical ontologies are complex knowledge representation systems. Biomedical ontologies support interdisciplinary research, interoperability of medical systems, and Electronic Healthcare Record (EHR) encoding. Ontologies represent knowledge using concepts (entities) linked by relationships. Ontologies may contain hundreds of thousands of concepts and millions of relationships. For users, the size and complexity of ontologies make it difficult to comprehend “the big picture” of an ontology\u27s content. For ontology editors, size and complexity make it difficult to uncover errors and inconsistencies. Errors in an ontology will ultimately affect applications that utilize the ontology. In prior studies abstraction networks (AbNs) were developed to provide a compact summary of an ontology\u27s content and structure. AbNs have been shown to successfully support ontology summarization and quality assurance (QA), e.g., for SNOMED CT and NCIt. Despite the success of these previous studies, several major, unaddressed issues affect the applicability and usability of AbNs. This thesis is broken into five major parts, each addressing one issue. The first part of this dissertation addresses the scalability of AbN-based QA techniques to large SNOMED CT hierarchies. Previous studies focused on relatively small hierarchies. The QA techniques developed for these small hierarchies do not scale to large hierarchies, e.g., Procedure and Clinical finding. A new type of AbN, called a subtaxonomy, is introduced to address this problem. Subtaxonomies summarize a subset of an ontology\u27s content. Several types of subtaxonomies and subtaxonomy-based QA studies are discussed. The second part of this dissertation addresses the need for summarization and QA methods for the twelve SNOMED CT hierarchies with no lateral relationships. Previously developed SNOMED CT AbN derivation methodologies, which require lateral relationships, cannot be applied to these hierarchies. The Tribal Abstraction Network (TAN) is a new type of AbN derived using only hierarchical relationships. A TAN-based QA methodology is introduced and the results of a QA review of the Observable entity hierarchy are reported. The third part focuses on the development of generic AbN derivation methods that are applicable to groups of structurally similar ontologies, e.g., those developed in the Web Ontology Language (OWL) format. Previously, AbN derivation techniques were applicable to only a single ontology at a time. AbNs that are applicable to many OWL ontologies are introduced, a preliminary study on OWL AbN granularity is reported on, and the results of several QA studies are presented. The fourth part describes Diff Abstraction Networks, which summarize and visualize the structural differences between two ontology releases. Diff Area Taxonomy and Diff Partial-area Taxonomy derivation methodologies are introduced and Diff Partial-area taxonomies are derived for three OWL ontologies. The Diff Abstraction Network approach is compared to the traditional ontology diff approach. Lastly, tools for deriving and visualizing AbNs are described. The Biomedical Layout Utility Framework is introduced to support the automatic creation, visualization, and exploration of abstraction networks for SNOMED CT and OWL ontologies

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. Description: The Foundational Model of Anatomy (FMA) Ontology provides a semantic framework for representing the anatomical entities and relationships that constitute the phenotypic organization of the human body. In this paper we describe recent enhancements to the neuroanatomical content of the FMA that models cytoarchitectural and morphological regions of the cerebral cortex, as well as white matter structure and connectivity. This modeling effort is driven by the need to correlate and reconcile the terms used in neuroanatomical labeling protocols. By providing an ontological framework that harmonizes multiple views of neuroanatomical organization, the FMA provides developers with reusable and computable knowledge for a range of biomedical applications. Conclusions: A requirement for facilitating the integration of basic and clinical neuroscience data from diverse sources is a well-structured ontology that can incorporate, organize, and associate neuroanatomical data. We applied the ontological framework of the FMA to align the vocabularies used by several human brain atlases, and to encode emerging knowledge about structural connectivity in the brain. We highlighted several use cases of these extensions, including ontology reuse, neuroimaging data annotation, and organizing 3D brain models

Thirty years of artificial intelligence in medicine (AIME) conferences: A review of research themes

Over the past 30 years, the international conference on Artificial Intelligence in MEdicine (AIME) has been organized at different venues across Europe every 2 years, establishing a forum for scientific exchange and creating an active research community. The Artificial Intelligence in Medicine journal has published theme issues with extended versions of selected AIME papers since 1998

The Gene Ontology Handbook

bioinformatics; biotechnolog

User-centered semantic dataset retrieval

Finding relevant research data is an increasingly important but time-consuming task in daily research practice. Several studies report on difficulties in dataset search, e.g., scholars retrieve only partial pertinent data, and important information can not be displayed in the user interface. Overcoming these problems has motivated a number of research efforts in computer science, such as text mining and semantic search. In particular, the emergence of the Semantic Web opens a variety of novel research perspectives. Motivated by these challenges, the overall aim of this work is to analyze the current obstacles in dataset search and to propose and develop a novel semantic dataset search. The studied domain is biodiversity research, a domain that explores the diversity of life, habitats and ecosystems. This thesis has three main contributions: (1) We evaluate the current situation in dataset search in a user study, and we compare a semantic search with a classical keyword search to explore the suitability of semantic web technologies for dataset search. (2) We generate a question corpus and develop an information model to figure out on what scientific topics scholars in biodiversity research are interested in. Moreover, we also analyze the gap between current metadata and scholarly search interests, and we explore whether metadata and user interests match. (3) We propose and develop an improved dataset search based on three components: (A) a text mining pipeline, enriching metadata and queries with semantic categories and URIs, (B) a retrieval component with a semantic index over categories and URIs and (C) a user interface that enables a search within categories and a search including further hierarchical relations. Following user centered design principles, we ensure user involvement in various user studies during the development process

Information Systems and Healthcare XXXIV: Clinical Knowledge Management Systems—Literature Review and Research Issues for Information Systems

Knowledge Management (KM) has emerged as a possible solution to many of the challenges facing U.S. and international healthcare systems. These challenges include concerns regarding the safety and quality of patient care, critical inefficiency, disparate technologies and information standards, rapidly rising costs and clinical information overload. In this paper, we focus on clinical knowledge management systems (CKMS) research. The objectives of the paper are to evaluate the current state of knowledge management systems diffusion in the clinical setting, assess the present status and focus of CKMS research efforts, and identify research gaps and opportunities for future work across the medical informatics and information systems disciplines. The study analyzes the literature along two dimensions: (1) the knowledge management processes of creation, capture, transfer, and application, and (2) the clinical processes of diagnosis, treatment, monitoring and prognosis. The study reveals that the vast majority of CKMS research has been conducted by the medical and health informatics communities. Information systems (IS) researchers have played a limited role in past CKMS research. Overall, the results indicate that there is considerable potential for IS researchers to contribute their expertise to the improvement of clinical process through technology-based KM approaches

Using structural and semantic methodologies to enhance biomedical terminologies

Biomedical terminologies and ontologies underlie various Health Information Systems (HISs), Electronic Health Record (EHR) Systems, Health Information Exchanges (HIEs) and health administrative systems. Moreover, the proliferation of interdisciplinary research efforts in the biomedical field is fueling the need to overcome terminological barriers when integrating knowledge from different fields into a unified research project. Therefore well-developed and well-maintained terminologies are in high demand. Most of the biomedical terminologies are large and complex, which makes it impossible for human experts to manually detect and correct all errors and inconsistencies. Automated and semi-automated Quality Assurance methodologies that focus on areas that are more likely to contain errors and inconsistencies are therefore important. In this dissertation, structural and semantic methodologies are used to enhance biomedical terminologies. The dissertation work is divided into three major parts. The first part consists of structural auditing techniques for the Semantic Network of the Unified Medical Language System (UMLS), which serves as a vocabulary knowledge base for biomedical research in various applications. Research techniques are presented on how to automatically identify and prevent erroneous semantic type assignments to concepts. The Web-based adviseEditor system is introduced to help UMLS editors to make correct multiple semantic type assignments to concepts. It is made available to the National Library of Medicine for future use in maintaining the UMLS. The second part of this dissertation is on how to enhance the conceptual content of SNOMED CT by methods of semantic harmonization. By 2015, SNOMED will become the standard terminology for EH R encoding of diagnoses and problem lists. In order to enrich the semantics and coverage of SNOMED CT for clinical and research applications, the problem of semantic harmonization between SNOMED CT and six reference terminologies is approached by 1) comparing the vertical density of SNOM ED CT with the reference terminologies to find potential concepts for export and import; and 2) categorizing the relationships between structurally congruent concepts from pairs of terminologies, with SNOMED CT being one terminology in the pair. Six kinds of configurations are observed, e.g., alternative classifications, and suggested synonyms. For each configuration, a corresponding solution is presented for enhancing one or both of the terminologies. The third part applies Quality Assurance techniques based on “Abstraction Networks” to biomedical ontologies in BioPortal. The National Center for Biomedical Ontology provides B ioPortal as a repository of over 350 biomedical ontologies covering a wide range of domains. It is extremely difficult to design a new Quality Assurance methodology for each ontology in BioPortal. Fortunately, groups of ontologies in BioPortal share common structural features. Thus, they can be grouped into families based on combinations of these features. A uniform Quality Assurance methodology design for each family will achieve improved efficiency, which is critical with the limited Quality Assurance resources available to most ontology curators. In this dissertation, a family-based framework covering 186 BioPortal ontologies and accompanying Quality Assurance methods based on abstraction networks are presented to tackle this problem