8,959 research outputs found

    The Semantic Automated Discovery and Integration (SADI) Web service Design-Pattern, API and Reference Implementation

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    Background. 
The complexity and inter-related nature of biological data poses a difficult challenge for data and tool integration. There has been a proliferation of interoperability standards and projects over the past decade, none of which has been widely adopted by the bioinformatics community. Recent attempts have focused on the use of semantics to assist integration, and Semantic Web technologies are being welcomed by this community.

Description. 
SADI – Semantic Automated Discovery and Integration – is a lightweight set of fully standards-compliant Semantic Web service design patterns that simplify the publication of services of the type commonly found in bioinformatics and other scientific domains. Using Semantic Web technologies at every level of the Web services “stack”, SADI services consume and produce instances of OWL Classes following a small number of very straightforward best-practices. In addition, we provide codebases that support these best-practices, and plug-in tools to popular developer and client software that dramatically simplify deployment of services by providers, and the discovery and utilization of those services by their consumers.

Conclusions.
SADI Services are fully compliant with, and utilize only foundational Web standards; are simple to create and maintain for service providers; and can be discovered and utilized in a very intuitive way by biologist end-users. In addition, the SADI design patterns significantly improve the ability of software to automatically discover appropriate services based on user-needs, and automatically chain these into complex analytical workflows. We show that, when resources are exposed through SADI, data compliant with a given ontological model can be automatically gathered, or generated, from these distributed, non-coordinating resources - a behavior we have not observed in any other Semantic system. Finally, we show that, using SADI, data dynamically generated from Web services can be explored in a manner very similar to data housed in static triple-stores, thus facilitating the intersection of Web services and Semantic Web technologies

    The Requirements for Ontologies in Medical Data Integration: A Case Study

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    Evidence-based medicine is critically dependent on three sources of information: a medical knowledge base, the patients medical record and knowledge of available resources, including where appropriate, clinical protocols. Patient data is often scattered in a variety of databases and may, in a distributed model, be held across several disparate repositories. Consequently addressing the needs of an evidence-based medicine community presents issues of biomedical data integration, clinical interpretation and knowledge management. This paper outlines how the Health-e-Child project has approached the challenge of requirements specification for (bio-) medical data integration, from the level of cellular data, through disease to that of patient and population. The approach is illuminated through the requirements elicitation and analysis of Juvenile Idiopathic Arthritis (JIA), one of three diseases being studied in the EC-funded Health-e-Child project.Comment: 6 pages, 1 figure. Presented at the 11th International Database Engineering & Applications Symposium (Ideas2007). Banff, Canada September 200

    Structure-based classification and ontology in chemistry

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    <p>Abstract</p> <p>Background</p> <p>Recent years have seen an explosion in the availability of data in the chemistry domain. With this information explosion, however, retrieving <it>relevant </it>results from the available information, and <it>organising </it>those results, become even harder problems. Computational processing is essential to filter and organise the available resources so as to better facilitate the work of scientists. Ontologies encode expert domain knowledge in a hierarchically organised machine-processable format. One such ontology for the chemical domain is ChEBI. ChEBI provides a classification of chemicals based on their structural features and a role or activity-based classification. An example of a structure-based class is 'pentacyclic compound' (compounds containing five-ring structures), while an example of a role-based class is 'analgesic', since many different chemicals can act as analgesics without sharing structural features. Structure-based classification in chemistry exploits elegant regularities and symmetries in the underlying chemical domain. As yet, there has been neither a systematic analysis of the types of structural classification in use in chemistry nor a comparison to the capabilities of available technologies.</p> <p>Results</p> <p>We analyze the different categories of structural classes in chemistry, presenting a list of patterns for features found in class definitions. We compare these patterns of class definition to tools which allow for automation of hierarchy construction within cheminformatics and within logic-based ontology technology, going into detail in the latter case with respect to the expressive capabilities of the Web Ontology Language and recent extensions for modelling structured objects. Finally we discuss the relationships and interactions between cheminformatics approaches and logic-based approaches.</p> <p>Conclusion</p> <p>Systems that perform intelligent reasoning tasks on chemistry data require a diverse set of underlying computational utilities including algorithmic, statistical and logic-based tools. For the task of automatic structure-based classification of chemical entities, essential to managing the vast swathes of chemical data being brought online, systems which are capable of hybrid reasoning combining several different approaches are crucial. We provide a thorough review of the available tools and methodologies, and identify areas of open research.</p

    Integrating Protein Data Resources through Semantic Web Services

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    Understanding the function of every protein is one major objective of bioinformatics. Currently, a large amount of information (e.g., sequence, structure and dynamics) is being produced by experiments and predictions that are associated with protein function. Integrating these diverse data about protein sequence, structure, dynamics and other protein features allows further exploration and establishment of the relationships between protein sequence, structure, dynamics and function, and thereby controlling the function of target proteins. However, information integration in protein data resources faces challenges at technology level for interfacing heterogeneous data formats and standards and at application level for semantic interpretation of dissimilar data and queries. In this research, a semantic web services infrastructure, called Web Services for Protein data resources (WSP), for flexible and user-oriented integration of protein data resources, is proposed. This infrastructure includes a method for modeling protein web services, a service publication algorithm, an efficient service discovery (matching) algorithm, and an optimal service chaining algorithm. Rather than relying on syntactic matching, the matching algorithm discovers services based on their similarity to the requested service. Therefore, users can locate services that semantically match their data requirements even if they are syntactically distinctive. Furthermore, WSP supports a workflow-based approach for service integration. The chaining algorithm is used to select and chain services, based on the criteria of service accuracy and data interoperability. The algorithm generates a web services workflow which automatically integrates the results from individual services.A number of experiments are conducted to evaluate the performance of the matching algorithm. The results reveal that the algorithm can discover services with reasonable performance. Also, a composite service, which integrates protein dynamics and conservation, is experimented using the WSP infrastructure

    Neurocognitive Informatics Manifesto.

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    Informatics studies all aspects of the structure of natural and artificial information systems. Theoretical and abstract approaches to information have made great advances, but human information processing is still unmatched in many areas, including information management, representation and understanding. Neurocognitive informatics is a new, emerging field that should help to improve the matching of artificial and natural systems, and inspire better computational algorithms to solve problems that are still beyond the reach of machines. In this position paper examples of neurocognitive inspirations and promising directions in this area are given

    The pharmaceuticalisation of security: molecular biomedicine, antiviral stockpiles, and global health security

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    Pharmaceuticals are now critical to the security of populations. Antivirals, antibiotics, next-generation vaccines, and antitoxins are just some of the new ‘medical countermeasures’ that governments are stockpiling in order to defend their populations against the threat of pandemics and bioterrorism. How has security policy come to be so deeply imbricated with pharmaceutical logics and solutions? This article captures, maps, and analyses the ‘pharmaceuticalisation’ of security. Through an in-depth analysis of the prominent antiviral medication Tamiflu, it shows that this pharmaceutical turn in security policy is intimately bound up with the rise of a molecular vision of life promulgated by the biomedical sciences. Caught in the crosshairs of powerful commercial, political, and regulatory pressures, governments are embracing a molecular biomedicine promising to secure populations pharmaceutically in the twenty-first century. If that is true, then the established disciplinary view of health as a predominantly secondary matter of ‘low’ international politics is mistaken. On the contrary, the social forces of health and biomedicine are powerful enough to influence the core practices of international politics – even those of security. For a discipline long accustomed to studying macro-level processes and systemic structures, it is in the end also our knowledge of the minute morass of molecules that shapes international relations

    A Semantic Framework for Declarative and Procedural Knowledge

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    In any scientic domain, the full set of data and programs has reached an-ome status, i.e. it has grown massively. The original article on the Semantic Web describes the evolution of a Web of actionable information, i.e.\ud information derived from data through a semantic theory for interpreting the symbols. In a Semantic Web, methodologies are studied for describing, managing and analyzing both resources (domain knowledge) and applications (operational knowledge) - without any restriction on what and where they\ud are respectively suitable and available in the Web - as well as for realizing automatic and semantic-driven work\ud ows of Web applications elaborating Web resources.\ud This thesis attempts to provide a synthesis among Semantic Web technologies, Ontology Research, Knowledge and Work\ud ow Management. Such a synthesis is represented by Resourceome, a Web-based framework consisting of two components which strictly interact with each other: an ontology-based and domain-independent knowledge manager system (Resourceome KMS) - relying on a knowledge model where resource and operational knowledge are contextualized in any domain - and a semantic-driven work ow editor, manager and agent-based execution system (Resourceome WMS).\ud The Resourceome KMS and the Resourceome WMS are exploited in order to realize semantic-driven formulations of work\ud ows, where activities are semantically linked to any involved resource. In the whole, combining the use of domain ontologies and work ow techniques, Resourceome provides a exible domain and operational knowledge organization, a powerful engine for semantic-driven work\ud ow composition, and a distributed, automatic and\ud transparent environment for work ow execution
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