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• W17288310 abstract "Biomedical ontologies not only capture a wealth of biological knowledge but also provide a representational system to support the integration and retrieval of biological information. Various biomedical ontologies are used by model organism databases to annotate biological entities to the literature and have become an essential part of high throughput experiments and bioinformatics research. We are exploring the power of ontology visualization to enhance the understanding of annotations by placing annotations in the graph context of the broader biological knowledge the ontology provides. Presenting annotations in this context provides a better understanding of the annotations because humans are adept at extracting patterns and information from graphical representations of complex data. INTRODUCTION Biological systems can be very complex but many aspects of biological system characterization have a wealth of biomedical knowledge accumulated over years of clinical and laboratory experience. Ontologies provide a shared understanding of a domain that is human intelligible and computer readable and, consequently, a representational system to support the integration and retrieval of this knowledge. As techniques of large-scale genomic analysis and functional gene annotation have progressed and are becoming more common, it is essential to find approaches to provide a comprehensive view of annotation sets. We are exploring the power of several widely used ontologies to provide a comprehensive graphical view of annotations by presenting the annotations visualized within an ontology relationship structure. By presenting annotations in the graph context we hope to provide a better understanding of the annotations because humans are adept at extracting patterns and information from graphical representations of complex data. BACKGROUND Ontologies can be used to abstract knowledge of a domain in a way that can be used by both by humans and computers by providing an explicit representation of the entities of interest and the relationships among them. In particular, biomedical ontologies representing various aspects of biology are being used for annotating entities to the literature and for integrating the diverse information resulting from the analysis of high-throughput experiments. Open Biomedical Ontologies (OBO) is an umbrella repository for well-structured controlled vocabularies for shared use across different biological and medical domains [1]. The OBO website contains a range of ontologies that are designed for biomedical domains. Some of the OBO ontologies, such as the Gene Ontology (GO), apply across all organisms. Others are more restricted in scope; for example, the Mammalian Phenotype Ontology (MP) is a phenotype ontology designed for specific taxonomic groups. The GO Project was established to provide structured, controlled, organism-independent vocabularies to describe gene functions [2] and, as a consequence, provides semantic standards for annotation of molecular attributes in different databases. Members of the GO Consortium supply annotations of gene products using this vocabulary. The GO and annotations made to GO provide consistent descriptions of gene products and a valuable resource for comparative functional analysis research. Currently, the three ontologies of GO contain nearly 20,000 terms [3]. The terms are organized in structures called directed acyclic graphs (DAGs) which differ from strict hierarchies in that a more specialized (granular) child term can have more than one less specialized parent term. In the GO a child can be related to a parent by either a ‘part of’ or ‘is a’ relationship. Mouse Genome Informatics (MGI) curators use the GO to annotate mouse genes from the literature. Currently, MGI has more than 100,000 annotations to more than 17,000 genes; approximately half of the annotations are manual" @default.
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• W17288310 title "Using ontology visualization to understand annotations and reason about them" @default.
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