Ontologies in bioinformatics and systems biology

Computer simulation is now becoming a central scientific paradigm of systems biology and the basic tool for the theoretical study and understanding of the complex mechanisms of living systems. The increase in the number and complexity of these models leads to the need for their collaborative development, reuse of models, and their verification, and the description of the computational experiment and its results. Ontological modeling is used to develop formats for knowledge-oriented mathematical modeling of biological systems. In this sense, ontology associated with the entire set of formats, supporting research in systems biology, in particular, computer modeling of biological systems and processes can be regarded as a first approximation to the ontology of systems biology. This review summarizes the features of the subject area (bioinformatics, systems biology, and biomedicine), the main motivation for the development of ontologies and the most important examples of ontological modeling and semantic analysis at different levels of the hierarchy of knowledge: the molecular genetic level, cellular level, tissue levels of organs and the body. Bioinformatics and systems biology is an excellent ground for testing technologies and efficient use of ontological modeling. Several dozens of verified basic reference ontologies now represent a source of knowledge for the integration and development of more complex domain models aimed at addressing specific issues in biomedicine and biotechnology. Further formalization and ontological accumulation of knowledge and the use of formal methods of analysis can take the entire cycle of research in systems biology to a new technological level

OntoQuery: easy-to-use web-based OWL querying

Summary: The Web Ontology Language (OWL) provides a sophisticated language for building complex domain ontologies and is widely used in bio-ontologies such as the Gene Ontology. The Protégé-OWL ontology editing tool provides a query facility that allows composition and execution of queries with the human-readable Manchester OWL syntax, with syntax checking and entity label lookup. No equivalent query facility such as the Protégé Description Logics (DL) query yet exists in web form. However, many users interact with bio-ontologies such as chemical entities of biological interest and the Gene Ontology using their online Web sites, within which DL-based querying functionality is not available. To address this gap, we introduce the OntoQuery web-based query utility. Availability and implementation: The source code for this implementation together with instructions for installation is available at http://github.com/IlincaTudose/OntoQuery. OntoQuery software is fully compatible with all OWL-based ontologies and is available for download (CC-0 license). The ChEBI installation, ChEBI OntoQuery, is available at http://www.ebi.ac.uk/chebi/tools/ontoquery. Contact: [email protected]

Structure-based classification and ontology in chemistry

<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

ONTOLOGIAS DE DOMÍNIO: ESTUDO DAS RELAÇÕES CONCEITUAIS E SUA APLICAÇÃO

Lack of theoretical bases and sound methodologies are problems that arise when building Ontologies. These constitute hindrance to the creation of inference rules for machine understanding. The study proposes systematization of relations found in the literature from Information Science, Terminology, Computer Science and Bioinformatics. Fundamental Categories in Information Science provide a domain representation model, but not the relation among them. In Computer Science relations are revealed, but not the context in which they occur. The systematization proposed aims at linking couple of categories (categorical relations) with relations properly (formal relations). A sample of definitions from Gene Ontology was analyzed to identify relations already mentioned in the literature and/or new ones. Despite the models for definition in Gene Ontology not always relations could be easily identified. It is concluded that different relations may arise in different domains and that systematic definitions are fundamental for the establishment of formal relations.No âmbito da elaboração de Ontologias, uma problemática que tem se colocado é a ausência de um padrão teórico-metodológico para sua elaboração, o que implica na criação a criação de regras de inferências consistentes que possam ser interpretáveis pela máquina. Propõe-se então uma sistematização das relações apresentadas nas literaturas da Ciência da Informação, da Terminologia, da Ciência da Computação e da Bioinformática. Na Ciência da Informação, as Categorias Fundamentais proporcionam um modelo de representação de um domínio, mas não explicitam a relação entre elas. A Ciência da Computação explicita as relações, mas não fornece um contexto. A sistematização realizada busca unir as duplas de categorias (relações categoriais) com as relações potenciais entre elas (relações formais). A partir da seleção de um corpus da Gene Ontology, foi feita uma análise das definições, a fim de identificar as relações já mencionadas na literatura ou descobrir a existência de novas relações. Apesar da Gene Ontology apresentar padrões para as definições, nem sempre foi possível identificar facilmente as relações. Conclui-se que relações diferentes podem surgir em diferentes domínios e que definições sistematizadas são indispensáveis para o estabelecimento seguro de relações formais