Catálogo de publicaciones - libros

Pharmacogenomics studies the involvement of interindividual variations of DNA sequence in different drug responses (especially adverse drug reactions). Knowledge Discovery in Databases (KDD) process is a means for discovering new pharmacogenomic knowledge in biological databases. However data complexity makes it necessary to guide the KDD process by representation of domain knowledge. Three domains at least are in concern: genotype, drug and phenotype. The approach described here aims at reusing whenever possible existing domain knowledge in order to build a modular formal representation of domain knowledge in pharmacogenomics. The resulting ontology is called SO-Pharm for Suggested Ontology for Pharmacogenomics. Various situations encountered during the construction process are analyzed and discussed. A preliminary validation is provided by representing with SO-Pharm concepts some well-known examples of pharmacogenomic knowledge.

Relational databases have been used to represent lexical knowledge since the days of machine-readable dictionaries. However, although software engineering provides a methodological framework for the construction of databases, most developing efforts focus on content, implementation and time-saving issues, and forget about the software engineering aspects of database construction. We have defined a methodology for the development of lexical resources that covers this and other aspects, by following a sound software engineering approach to formally represent knowledge. Nonetheless, the conceptual model from which it departs has some major limitations that need to be overcome. Based on a short analysis of common problems in existing lexical resources, we present an upgraded conceptual model as a first step towards the methodological development of a hierarchically organized concept-based terminology database, to improve the access to medical information as part of the SINAMED and ISIS projects.

Large knowledge bases integrating different domains can provide a foundation for new applications in biology such as data mining or automated reasoning. The traditional approach to the construction of such knowledge bases is manual and therefore extremely time consuming. The ubiquity of the internet now makes large-scale community collaboration for the construction of knowledge bases, such as the successful online encyclopedia “Wikipedia”, possible.

We propose an extension of this model to the collaborative annotation of molecular data. We argue that a semantic wiki provides the functionality required for this project since this can capitalize on the existing representations in biological ontologies. We discuss the use of a different relationship model than the one provided by RDF and OWL to represent the semantic data. We argue that this leads to a more intuitive and correct way to enter semantic content in the wiki. Furthermore, we show how formal ontologies could be used to increase the usability of the software through type-checking and automatic reasoning.

In this paper we present our progress on a framework for collection and presentation of biomedical information through ontology-based mediation. The framework is built on top of a methodology for computational prioritization of candidate disease genes, called Endeavour. Endeavour prioritizes genes based on their similarity with a set of training genes while using a wide variety of information sources. However, collecting information from different sources is a difficult process and can lead to non-flexible solutions. In this paper we describe an ontology-based mediation framework for efficient retrieval, integration, and visualization of the information sources Endeavour uses. The described framework allows to (1) integrate the information sources on a conceptual level, (2) provide transparency to the user, (3) eliminate ambiguity and (4) increase efficiency in information display.

Integration of heterogeneous sources is a means to offer the user an access to multiple information sources in a unified way through queries submitted to a global schema. We propose a semantic web-based mediator model, to provide unified access to various sources which may be both structured (database systems) and unstructured (textual medical reports, scientific publications, etc.). The mediator level is composed of the global ontology and a set of ontologies which make it possible to characterize sources (one or several ontologies by source). Unstructured sources are seen through their Semantic Document Representation obtained by a semantic characterization process. A reverse engineering process is then applied on each Semantic Document Representation schema and each structured source schema in order to provide semi-automatically a set of local ontologies. These local ontologies are articulated around the global schema following the global centric approach. A service called Terminology Server (ServO) is used to perform queries and manage the ontologies. The ontology-based query model combines databases and information retrieval techniques. We illustrate this approach with a case study in the brain disease field but it is sufficiently generic to be used in other domains.

Reactome (www.reactome.org) is a curated database describing very diverse biological processes in a computationally accessible format. The data is provided by experts in the field and subject to a peer review process. The core unit of the Reactome data model is the reaction. The entities participating in reactions form a network of biological interactions. Reactions are grouped into pathways. Reactome data are cross-referenced to a wide selection of publically available databases (such as UniProt, Ensembl, GO, PubMed), facilitating overall integration of biological data. In addition to the manually curated, mainly human reactions, electronically inferred reactions to a wide range of other species, are presented on the website. All Reactome reactions are displayed as arrows on a . The tool allows visualisation of user-supplied data by colouring the . Reactome data are freely available and can be downloaded in a number of formats.

Many researches related to storing XML data have been performed and some of them proposed methods to improve the performance of databases by reducing the joins between tables. Those methods are very efficient in deriving and optimizing tables from a DTD or XML schema in which elements and attributes are defined. Nevertheless, those methods are not effective in an XML schema for biological information such as microarray data because even though microarray data have complex hierarchies just a few core values of microarray data repeatedly appear in the hierarchies. In this paper, we propose a new algorithm to extract core features which is repeatedly occurs in an XML schema for biological information, and elucidate how to improve classification speed and efficiency by using a decision tree rather than pattern matching in classifying structural similarities. We designed a database for storing biological information using features extracted by our algorithm. By experimentation, we showed that the proposed classification algorithm also reduced the number of joins between tables.

We propose an abstract model for scientific protocols, where several atomic operators are proposed for protocol composition. We distinguish two different layers associated with scientific protocols: design and implementation, and discuss the mapping between them. We illustrate our approach with a representative example and describe ProtocolDB, a scientific protocol repository currently in development. Our approach benefits scientists by allowing the archiving of scientific protocols with the collected data sets to constitute a scientific portfolio for the laboratory to query, compare and revise protocols.

Providing elementary resources to research institutes, storing a part of the world’s biodiversity and carrying out essential research, biological resource centers (BRCs) fulfil an important role in contemporary life sciences. There are currently over 500 BRCs registered at the World Data Center for Microorganisms. All of these institutes offer information on their cultures independently and there is no efficient linkage between the biological material and its related data held by information providers such as Genbank, Swissprot and PubMed. As a result, researchers searching for information on microbial strains or species frequently encounter inconsistencies. This paper presents the StrainInfo.net bioportal for integrating the information provided by BRCs and third party information holders. It is shown how intelligent data integration is the best protection against information overkill and is a necessary precursor for more advanced data mining to further exploit the assets of BRCs. To establish a solid service to the research community, the suggested integration is accomplished following the approach. Initially implemented as a web interface, the underlying system also provides possibilities for setting up web services. The StrainInfo.net bioportal will be made available at http://www.StrainInfo.net.

The Trading Agent Competition (TAC) is an international forum which promotes high-quality research regarding the trading agent problem. One of the TAC competitive scenarios is Supply Chain Management (SCM) where six agents compete by buying components, assembling PCs from these components and selling the manufactured PCs to customers. The idea of TAC SCM is the development of agent-based architectures that implement wide-applicable business strategies which efficiently manage cross-enterprise processes. In this paper, we analyze the TAC SCM environment and describe the main features of the CrocodileAgent, our entry in the TAC SCM Competition.