http://precedings.nature.com/tags/ontology Recently posted documents tagged with 'ontology' Nature Publishing Group en Nature Precedings http://precedings.nature.com/images/header_logo.gif http://precedings.nature.com http://dx.doi.org/10.1038/npre.2009.4001.1 The syntax of LBFO represents the initial step toward the creation of a rigorously characterized, recursively defined, artificial language for the sole purpose of ontology development. The underlying idea is that maximally fruitful application of ontology requires accurate representation of reality in accordance with current textbook science. Hence, creating a robust, accurate representation of reality is a fundamental concern. An ontology represents general types of entities and relations between them. A domain ontology represents the general types and relations for a given domain of research. A top-level ontology represents the general types of entities in any domain of research. Ontologies serve many purposes in computerized collection, management, and storage of data. These applications include enhancement of storage and retrieval in a data system, integration of diverse systems, integration of semantic content on the web, and annotation of publications in a library setting.Successful application of ontologies has led to the creation of languages with the special purpose of implementing ontologies. A formalized ontology is an ontology expressed in accordance with the grammatical formation rules of an artificial language. Some existing ontology languages have been developed in order to serve specific functions that require expressibility limitations and expression of information in a manner that contributes to human misunderstanding and error. The most potentially detrimental effect is risked when an ontology is constructed in a language designed exclusively for computerized implementation. The result is a skewed representation of salient features of reality. An ontology development language has two purposes: one is to represent reality as accurately and completely as possible, the other is to achieve this in a manner that facilitates computerized implementation: these goals conflict. Validation requires expert human consensus, hence, an ontology should be developed in a language that is understandable to domain experts. However, such a language must be computer tractable, i.e., there must be a correspondence between the information expressed with a sentence and its grammatical structure such that information can be processed on the basis of syntax alone. LBFO will facilitate providing definitions and characterizations of features of reality in a way conformant with Basic Formal Ontology (BFO) thus ensuring maximal rigor and clarity. Since LBFO is a multi-sorted language, LBFO has resources to represent the ontological categories found in BFO and the universals defined in their terms in an economical and at the same time user-friendly way. BFO is a realist ontology in that it recognizes universals as an part of the world. BFO also recognizes the existence of both processes and continuants. A continuant is an individual that exists in full at each point in time in which it exists, a process is an individual that exists in stages and happens through time. Unlike a continuant, a process cannot be identified with any single stage at which it exists at a specific point in time. Capitalized variables range over universals, while lower-case variables range over individuals. Universal constants are upper-case. Individual constants are lower-case. The syntax of LBFO also distinguishes in a straightforward manner between variables for continuants, processes, and times. The syntax of LBFO contains precisely expressed grammatical-formation rules, so that its variables cannot be combined in a manner that results in category errors. The predicates of LBFO are such that the ontological category from which terms representing entities can be taken as arguments is specified in advance. Sentences which express category errors are not grammatically correct in LBFO.Since the demand for implementation often outstrips the demand for accurate representation, stand-alone ontologies are often left by the wayside. LBFO can serve as a bridge between domain experts, knowledge engineers, and implementation languages. The semantic apparatus of an FOL system serves as the basis for the models developed for implementation languages such as OWL and RDF. FOL is also a segregated dialect of Common Logic so there is a link to that international standard; hence, there is potential to develop middle-ware that maps LBFO to the variety of implementation languages that exist both now and in the future. Though there is much work to be done in perfecting LBFO, this first step in the process provides hope for achieving the goal of facilitating maximally accurate, rigorous representations of general features of reality. http://dx.doi.org/10.1038/npre.2009.4001.1 Mon, 23 Nov 2009 15:36:02 UTC LBFO: toward an artificial language for ontology development doi:10.1038/npre.2009.4001.1 2009-11-23 Leonard F. Jacuzzo Nature Precedings 2009-11-23T15:36:02Z Poster Bioinformatics http://creativecommons.org/licenses/by/3.0/ http://dx.doi.org/10.1038/npre.2009.3980.1 Drosophila neuro-anatomical data is scattered across a large, diverse literature dating back over 75 years and a growing number of community databases. Lack of a standardized nomenclature for neuro-anatomy makes comparison and searching this growing data-set extremely arduous. A recent standardization effort (BrainName; Manuscript in preparation) has produced a segmented, 3D model of the Drosophila brain annotated with a controlled vocabulary. We are formalizing these developments to produce a web-based ontology-linked atlas in which gross brain anatomy is defined, in part, by labeled volumes in a standard reference brain.We have developed new relations that allow us to use this well-defined gross anatomy as a substrate to define neuronal types according to where they fasciculate and innervate as well as to record the neurotransmitters they release, their lineage and functions. The resulting ontology will provide a vocabulary for annotation and a means for integrative queries of neurobiological data.The ontology and associated images, queries and annotations will be integrated into the Virtual Fly Brain website. This will provide a resource that biologists can use to browse annotated images of Drosophila neuro-anatomy and to get answers to questions about that anatomy and related data, without any need for ontology expertise. http://dx.doi.org/10.1038/npre.2009.3980.1 Tue, 17 Nov 2009 11:24:20 UTC Virtual Fly Brain: An ontology-linked schema of the Drosophila Brain doi:10.1038/npre.2009.3980.1 2009-11-17 David J. Osumi-Sutherland Nature Precedings 2009-11-17T11:24:20Z Poster Neuroscience Bioinformatics http://creativecommons.org/licenses/by/3.0/ http://dx.doi.org/10.1038/npre.2009.3970.1 The developers of the Ontology of Biomedical Investigations (OBI) primarily use Protégé for editing. However, adding many classes with similar patterns of logical definition is time consuming, error prone, and requires the editor to have some expertise in OWL. Therefore, the process is poorly suited for a large number of domain experts who have limited experience Protégé and ontology development. We have developed a procedure to ease this task and allow such domain experts to add terms to the ontology in a way that both effectively includes complex logical definitions yet requires minimal manual intervention by OBI developers. The procedure is based on editing a Quick Term Template in a spreadsheet format which is subsequently converted into an OWL file. This procedure promises to be a robust and scalable approach for ontology enrichment. http://dx.doi.org/10.1038/npre.2009.3970.1 Mon, 16 Nov 2009 13:28:02 UTC Overcoming the Ontology Enrichment Bottleneck with Quick Term Templates doi:10.1038/npre.2009.3970.1 2009-11-16 Philippe Rocca-Serra Nature Precedings 2009-11-16T13:28:02Z Presentation Bioinformatics http://creativecommons.org/licenses/by/3.0/ http://precedings.nature.com/documents/3971/version/1 An ontological representation of the entities relevant to biological research is urgently needed. The cell ontology developed by Bard and colleagues (CL) (Bard et al. 2005) makes a significant contribution towards fulfilling this need by providing an ontology of cell types. The CL has already proven useful for data annotation (e.g. Grumbling et al. 2006), although the ontology’s potential utility goes well beyond that specific application. For example, using the number of distinct cell types in an organism as a measure of biological complexity, Vogel and Chothia (2006) compared the proteomes of 38 organisms of varying complexity and identified patterns in the evolution and expansion of protein domain superfamilies. This work has implications for some of the fundamental questions in biology, such as understanding the processes by which physiology becomes more intricate, new cell types arise, and biological complexity increases. While Vogel and Chothia did not yet utilize the CL for this work, they cite Bard et al. (2005) and describe the ontology’s value for improving and extending their analysis. Thus, in addition to its great utility for database annotation, the CL has the potential to play a significant role in basic scientific inquiry.The prospect of using the CL (and other ontologies) for this type of scientific research is extremely exciting but also imposes requirements on the level of formal rigor applied in ontology development, on the adequacy of the ontology as a representation of reality, and on its adherence to community standards of best practice. It is with these things in mind that I examined the CL to determine whether any revision would be required before my research group could use it for scientific research.After carefully evaluating the CL, my overall impression is that it does not possess the rigor and exactness required of a reference ontology. Furthermore, the problems I see are significant enough that it would be difficult for my research group to use the CL as our application ontology. While some of the problems could be resolved by changing a relation or rewriting a definition, others would require careful rethinking of the ontology’s foundation, because they involve the scope and organizing principle of the ontology as a whole. http://precedings.nature.com/documents/3971/version/1 Thu, 12 Nov 2009 17:05:38 UTC Evaluation of the Cell Ontology (CL) hdl:10101/npre.2009.3971.1 2009-11-12 Lindsay G. Cowell Nature Precedings 2009-11-12T17:05:38Z Manuscript Bioinformatics http://creativecommons.org/licenses/by/3.0/ http://dx.doi.org/10.1038/npre.2009.3868.1 BioPortal (http://bioportal.bioontology.org) is an open repository of biomedical ontologies that provides programmatic and web-based access to ontologies developed in OBO, OWL, Protégé frames, and RDF. Features include browsing, searching, and visualization of ontologies. Searching of integrated data resources is also possible through ontology-based indexing of biomedical resources with BioPortal ontologies. http://dx.doi.org/10.1038/npre.2009.3868.1 Fri, 16 Oct 2009 11:30:09 UTC BioPortal: Ontologies and Integrated Data Resources at the Click of a Mouse doi:10.1038/npre.2009.3868.1 2009-10-16 Patricia L. Whetzel Nature Precedings 2009-10-16T11:30:09Z Poster Bioinformatics http://creativecommons.org/licenses/by/3.0/ http://dx.doi.org/10.1038/npre.2009.3806.1 The Experimental Factor Ontology (www.ebi.ac.uk/efo) is an application focused ontology modelling the experimental factors in ArrayExpress. The ontology has been developed to increase the richness of the annotations that are currently made in the ArrayExpress repository, to promote consistent annotation, to facilitate automatic annotation and to integrate external data. The methodology employed in the development of EFO involves construction of mappings to multiple existing domain specific ontologies, such as the Disease Ontology and Cell Type Ontology. http://dx.doi.org/10.1038/npre.2009.3806.1 Fri, 25 Sep 2009 21:15:47 UTC Developing an application ontology for annotation of experimental variables – Experimental Factor Ontology doi:10.1038/npre.2009.3806.1 2009-09-25 Tomasz Adamusiak Nature Precedings 2009-09-25T21:15:47Z Poster Bioinformatics http://creativecommons.org/licenses/by/3.0/ http://dx.doi.org/10.1038/npre.2009.3714.1 We present ChemAxiom as the first ontological framework for chemistry in science. ChemAxiom enables discourse about chemical objects in a computable language and is useful for the management of chemical concepts and data, the retrospective typing of resources, the identification of ambiguity and supports chemical text mining. http://dx.doi.org/10.1038/npre.2009.3714.1 Thu, 03 Sep 2009 13:08:10 UTC ChemAxiom – An Ontological Framework for Chemistry in Science doi:10.1038/npre.2009.3714.1 2009-09-03 Nico Adams Nature Precedings 2009-09-03T13:08:10Z Manuscript Chemistry Bioinformatics http://creativecommons.org/licenses/by/3.0/ http://dx.doi.org/10.1038/npre.2009.3686.1 AbstractWe, participants in the Translational Medicine Ontology activity of the World Wide Web Consortium’s Health Care and Life Sciences Interest Group (http://esw.w3.org/topic/HCLSIG) and members of the National Center for Biomedical Ontology (http://bioontology.org/), are developing a high-level, patient-centric ontology for translational medicine which will draw on existing domain ontologies and allow the integration of data throughout the drug development process.IntroductionThe pharmaceutical industry has historically focused on the development of novel blockbuster drugs. There is now an increasing focus on personalized medicines, requiring the right patients to receive the right drug at the right dose. In order to develop a tailored drug, manufacturers need to identify biomarkers that will indicate how a given patient will respond to a particular treatment. Biomarkers can also be used to demonstrate the comparative effectiveness of drugs, which is increasingly required by payers. Such translational medicine strategies require that traditionally separate data sets from early drug discovery through to patients in the clinical setting be integrated, and presented, queried and analyzed collectively. Ontologies can be used to drive such data integration and analysis; however, at present few ontologies exist that bridge genomics, chemistry and the medical domain.The Translational Medicine Ontology, an application ontology that bridges the diverse areas of translational medicine, draws on existing domain ontologies where appropriate and will provide a framework centered on less than 50 types of entities.GoalsThe Translational Medicine Ontology will facilitate data integration from diverse areas of translational medicine such as discovery research, hypothesis management, formulation, clinical trials, and clinical research. It will serve as a template for further ontology development, enabling scientists to answer interesting and currently difficult questions more easily, especially those about data that are typically hosted by different functional areas. The ontology will provide a framework for the modeling of patient-centric information, which is essential for tailoring drugs.Methodology We have identified a set of 17 roles played by people across health care and the life sciences and collected (1) relevant questions, (2) the entities that those questions involve, and (3) applicable extant domain ontologies.1 Types of entities include: disease, drug, patient, target, gene, risk, pathway, population, compound, phenotype, and treatment.Next steps will involve identifying use cases based on those questions, determining which entities to build into the ontology and aligning them with BFO,2 an upper-level ontology, to aid interoperability between domain ontologies. We will use one use case to test the Translational Medicine Ontology by building a data integration application based on it.ConclusionThis project seeks to develop a patient-centric application ontology for translational medicine, as a collaborative effort between groups in industry and academia. The presentation will highlight our methodology, work to date, and future steps. 1. W3C Site 2. iformis http://dx.doi.org/10.1038/npre.2009.3686.1 Wed, 26 Aug 2009 17:14:14 UTC Creating a Translational Medicine Ontology doi:10.1038/npre.2009.3686.1 2009-08-26 Christine Denney Nature Precedings 2009-08-26T17:14:14Z Poster Developmental Biology Genetics & Genomics Immunology Molecular Cell Biology Neuroscience Pharmacology Bioinformatics http://creativecommons.org/licenses/by/3.0/ http://dx.doi.org/10.1038/npre.2009.3623.1 The goal of OBI is to enable a formal representation of biomedical investigations that captures the experimental evidence on which their findings are based. The scope of OBI includes: materials made in and produced for investigations, research objectives, experimental protocols, roles of people in investigations and processing and publication of data gathered in investigations. Use of OBI will allow comparison of experimental data from the wide array of scientific disciplines represented by domain experts in the OBI consortium. OBI follows the principles laid out by the OBO foundry, and integrates tightly with other foundry candidate ontologies, such as GO (www.geneontology.org) and ChEBI (www.ebi.ac.uk/chebi/) whose terms are used to describe biological reality. The use of OBI by the scientific community to represent or annotate their investigations within electronic data resources will facilitate interdisciplinary data synthesis, enable access to their data on the semantic web and improve third-party understanding of information related to life-science and clinical investigations. http://dx.doi.org/10.1038/npre.2009.3623.1 Mon, 17 Aug 2009 15:37:47 UTC Ontology for Biomedical Investigations doi:10.1038/npre.2009.3623.1 2009-08-17 Bjoern Peters Nature Precedings 2009-08-17T15:37:47Z Poster Biotechnology Bioinformatics http://creativecommons.org/licenses/by/3.0/ http://precedings.nature.com/documents/3615/version/1 We describe an approach to characterize genes or phenotypes via ontology fingerprints which are composed of Gene Ontology (GO) terms overrepresented among those PubMed abstracts linked to the genes or phenotypes. We then quantify the biological relevance between genes and phenotypes by comparing their ontology fingerprints to calculate a similarity score. We validated this approach by correctly identifying genes belong to their biological pathways with high accuracy, and applied this approach to evaluate GWA study by ranking genes associated with the lipid concentrations in plasma as well as to prioritize genes within linkage disequilibrium (LD) block. We found that the genes with highest scores were: ABCA1, LPL, and CETP for HDL; LDLR, APOE and APOB for LDL; and LPL, APOA1 and APOB for triglyceride. In addition, we identified some top ranked genes linking to lipid metabolism from the literature even in cases where such knowledge was not reflected in current annotation of these genes. These results demonstrate that ontology fingerprints can be used effectively to prioritize genes from GWA studies for experimental validation. http://precedings.nature.com/documents/3615/version/1 Fri, 14 Aug 2009 20:52:16 UTC Using Ontology Fingerprints to evaluate genome-wide association study results hdl:10101/npre.2009.3615.1 2009-08-14 W. Jim Zheng Nature Precedings 2009-08-14T20:52:16Z Manuscript Genetics & Genomics Bioinformatics http://creativecommons.org/licenses/by/3.0/