liu.seSearch for publications in DiVA
Change search
Refine search result
1 - 19 of 19
CiteExportLink to result list
Permanent link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • oxford
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Rows per page
  • 5
  • 10
  • 20
  • 50
  • 100
  • 250
Sort
  • Standard (Relevance)
  • Author A-Ö
  • Author Ö-A
  • Title A-Ö
  • Title Ö-A
  • Publication type A-Ö
  • Publication type Ö-A
  • Issued (Oldest first)
  • Issued (Newest first)
  • Created (Oldest first)
  • Created (Newest first)
  • Last updated (Oldest first)
  • Last updated (Newest first)
  • Disputation date (earliest first)
  • Disputation date (latest first)
  • Standard (Relevance)
  • Author A-Ö
  • Author Ö-A
  • Title A-Ö
  • Title Ö-A
  • Publication type A-Ö
  • Publication type Ö-A
  • Issued (Oldest first)
  • Issued (Newest first)
  • Created (Oldest first)
  • Created (Newest first)
  • Last updated (Oldest first)
  • Last updated (Newest first)
  • Disputation date (earliest first)
  • Disputation date (latest first)
Select
The maximal number of hits you can export is 250. When you want to export more records please use the Create feeds function.
  • 1.
    Backofen, Rolf
    et al.
    Friedrich-Schiller-Universität Jena, Germany.
    Badea, Liviu
    National Institute for Research and Development in Informatics, Bucharest, Romania.
    Barahona, Pedro
    Universidade Nova de Lisboa, Portugal.
    Berndtsson, Mikael
    University of Skövde, Sweden.
    Burger, Albert
    Heriot-Watt University/MRC Human GeneticsUnit, Edinburgh, UK.
    Dawelbait, Gihan
    Technische Universität Dresden, Germany.
    Doms, Andreas
    Technische Universität Dresden, Germany.
    Fages, Francois
    INRIA Rocquencourt, Paris, France.
    Hotaran, Anca
    National Institute for Research and Development in Informatics, Bucharest, Romania.
    Jakoniené, Vaida
    Linköping University, The Institute of Technology. Linköping University, Department of Computer and Information Science, IISLAB - Laboratory for Intelligent Information Systems.
    Krippahl, Ludwig
    Universidade Nova de Lisboa, Portugal.
    Lambrix, Patrick
    Linköping University, The Institute of Technology. Linköping University, Department of Computer and Information Science, IISLAB - Laboratory for Intelligent Information Systems.
    McLeod, Kenneth
    Heriot-Watt University/MRC Human GeneticsUnit, Edinburgh, UK.
    Nutt, Werner
    Heriot-Watt University/MRC Human GeneticsUnit, Edinburgh, UK.
    Olsson, Bjorn
    University of Skövde, Sweden.
    Schroeder, Michael
    Technische Universität Dresden, Germany.
    Schroiff, Anna
    University of Skövde, Sweden.
    Royer, Luc
    Technische Universität Dresden, Germany.
    Soliman, Sylvain
    INRIA Rocquencourt, Paris, France.
    Tan, He
    Linköping University, The Institute of Technology. Linköping University, Department of Computer and Information Science, IISLAB - Laboratory for Intelligent Information Systems.
    Tilivea, Doina
    National Institute for Research and Development in Informatics, Bucharest, Romania.
    Will, Sebastian
    Friedrich-Schiller-Universit¨at Jena, Germany.
    Requirements and specification of bioinformatics use cases2005Report (Other academic)
  • 2.
    Backofen, Rolf
    et al.
    Albert-Ludwigs-universität Freiburg, Germany.
    Burger, Albert
    Heriot-Watt university Edinburgh, UK.
    Busch, Anke
    Albert-Ludwigs-universität Freiburg, Germany.
    Dawelbait, Gihan
    TU Dresden, Germany.
    Fages, Francois
    INRIA Rocquencourt Paris, France.
    Jakoniené, Vaida
    Linköping University, Department of Computer and Information Science, Database and information techniques. Linköping University, The Institute of Technology.
    Lambrix, Patrick
    Linköping University, Department of Computer and Information Science, Database and information techniques. Linköping University, The Institute of Technology.
    McLeod, Kenneth
    Heriot-Watt university Edinburgh, UK.
    Soliman, Sylvain
    INRIA Rocquencourt Paris, France.
    Tan, He
    Linköping University, Department of Computer and Information Science, Database and information techniques. Linköping University, The Institute of Technology.
    Will, Sebastian
    Albert-Ludwigs-universität Freiburg, Germany.
    Implementation of prototypes2007Report (Other academic)
  • 3.
    Backofen, Rolf
    et al.
    Friedrich-Schiller-Universität Jena, Germany.
    Mike, Badea
    Victoria University of Manchester, UK.
    Barahona, Pedro
    FCT-UNL, Lisbon.
    Burger, Albert
    Harriot-Watt University, Edinburgh, UK.
    Dawelbait, Gihan
    Technical University of Dresden, Germany.
    Doms, Andreas
    Technical University of Dresden, Germany.
    Fages, Francois
    INRIA Rocquencourt, France.
    Hotaran, Anca
    National Institute for Research and Development in Informatics, Romania.
    Jakoniené, Vaida
    Linköping University, The Institute of Technology. Linköping University, Department of Computer and Information Science, IISLAB - Laboratory for Intelligent Information Systems.
    Krippahl, Ludwig
    FCT-UNL, Lisbon.
    Lambrix, Patrick
    Linköping University, The Institute of Technology. Linköping University, Department of Computer and Information Science, IISLAB - Laboratory for Intelligent Information Systems.
    McLeod, Kenneth
    Harriot-Watt University, Edinburgh, UK.
    Möller, Steffen
    Universität Rostock, Germany.
    Nutt, Werner
    Harriot-Watt University, Edinburgh, UK.
    Olsson, Björn
    University of Skövde, Sweden.
    Schroeder, Michael
    Technical University of Dresden, Germany.
    Soliman, Sylvain
    INRIA Rocquencourt, France.
    Tan, He
    Linköping University, The Institute of Technology. Linköping University, Department of Computer and Information Science, IISLAB - Laboratory for Intelligent Information Systems.
    Tilivea, Doina
    National Institute for Research and Development in Informatics, Romania.
    Will, Sebastian
    Friedrich-Schiller-Universität Jena, Germany.
    Usage of bioinformatics tools and identification of information sources2005Report (Other academic)
  • 4.
    Doms, Andreas
    et al.
    Technische Universität Dresden, Germany.
    Jakoniené, Vaida
    Linköping University, Department of Computer and Information Science, Database and information techniques. Linköping University, The Institute of Technology.
    Lambrix, Patrick
    Linköping University, Department of Computer and Information Science, Database and information techniques. Linköping University, The Institute of Technology.
    Schroeder, Michael
    Technische Universität Dresden, Germany.
    Wächter, Thomas
    Technische Universität Dresden, Germany.
    Ontologies and Text Mining as a Basis for a Semantic Web for the Life Sciences2006In: Reasoning Web, Second International Summer School: Summer School 2006, Lisbon, Portugal, September 4-8, 2006, Tutorial Lectures / [ed] Pedro BarahonaFrançois Bry, Enrico Franconi, Nicola Henze et, Springer Berlin/Heidelberg, 2006, p. 164-183Chapter in book (Refereed)
    Abstract [en]

    This book presents thoroughly arranged tutorial papers corresponding to lectures given by leading researchers at the Second International Summer School on Reasoning Web in Lisbon, Portugal, in September 2006. Building on the predessor school held in 2005 and published as LNCS 3564, the ten tutorial lectures presented provide competent coverage of current topics in semantic Web research and development.

  • 5.
    Jakoniene, Vaida
    et al.
    Linköping University, Department of Computer and Information Science, IISLAB - Laboratory for Intelligent Information Systems. Linköping University, The Institute of Technology.
    Lambrix, Patrick
    Linköping University, Department of Computer and Information Science, IISLAB - Laboratory for Intelligent Information Systems. Linköping University, The Institute of Technology.
    Monitoring performance and access to biological databases2002In: 3rd swedish annual workshop in bioinformatics for PhD students and Postdocs, 2002Conference paper (Other academic)
  • 6.
    Jakoniene, Vaida
    et al.
    Linköping University, Department of Computer and Information Science, IISLAB - Laboratory for Intelligent Information Systems. Linköping University, The Institute of Technology.
    Nilsson, Roland
    Proceedings of the Fourth Swedish Bioinformatics Workshop for PhD students and PostDocs2003Conference proceedings (editor) (Other academic)
  • 7.
    Jakoniené, Vaida
    Linköping University, Department of Computer and Information Science, IISLAB - Laboratory for Intelligent Information Systems. Linköping University, The Institute of Technology.
    A study in integrating multiple biological data sources2005Licentiate thesis, monograph (Other academic)
    Abstract [en]

    Life scientists often have to retrieve data from multiple biological data sources to solve their research problems. Although many data sources are available, they vary in content, data format, and access methods, which often vastly complicates the data retrieval process. The user must decide which data sources to access and in which order, how to retrieve the data and how to combine the results - in short, the task of retrieving data requires a great deal of effort and expertise on the part of the user.

    Information integration systems aim to alleviate these problems by providing a uniform (or even integrated) interface to biological data sources. The information integration systems currently available for biological data sources use traditional integration approaches. However, biological data and data sources have unique properties which introduce new challenges, requiring development of new solutions and approaches.

    This thesis is part of the BioTrifu project, which explores approaches to integrating multiple biological data sources. First, the thesis describes properties of biological data sources and existing systems that enable integrated access to them. Based on the study, requirements for systems integrating biological data sources are formulated and the challenges involved in developing such systems are discussed. Then, the thesis presents a query language and a high-level architecture for the BioTrifu system that meet these requirements. An approach to generating a query plan in the presence of alternative data sources and ways to integrate the data is then developed. Finally, the design and implementation of a prototype for the BioTrifu system are presented.

  • 8.
    Jakoniené, Vaida
    et al.
    Linköping University, Department of Computer and Information Science, Database and information techniques. Linköping University, The Institute of Technology.
    Lambrix, Patrick
    Linköping University, Department of Computer and Information Science, Database and information techniques. Linköping University, The Institute of Technology.
    A tool for evaluating strategies for grouping of biological data2006In: Seventh Swedish Bioinformatics Workshop for PhD students and PostDocs,2006, 2006, p. 23-24Conference paper (Other academic)
  • 9.
    Jakoniené, Vaida
    et al.
    Linköping University, The Institute of Technology. Linköping University, Department of Computer and Information Science, IISLAB - Laboratory for Intelligent Information Systems.
    Lambrix, Patrick
    Linköping University, The Institute of Technology. Linköping University, Department of Computer and Information Science, IISLAB - Laboratory for Intelligent Information Systems.
    Implementation of a System for Integrated Access to Biological Data Sources2004In: Bioinformatics 2004,2004, 2004, p. 44-44Conference paper (Other academic)
  • 10.
    Jakoniené, Vaida
    et al.
    Linköping University, Department of Computer and Information Science, Database and information techniques. Linköping University, The Institute of Technology.
    Lambrix, Patrick
    Linköping University, Department of Computer and Information Science, Database and information techniques. Linköping University, The Institute of Technology.
    Information integration systems for biological data source requirements and opportunities2006Report (Other (popular science, discussion, etc.))
  • 11.
    Jakoniené, Vaida
    et al.
    Linköping University, Department of Computer and Information Science, Database and information techniques. Linköping University, The Institute of Technology.
    Lambrix, Patrick
    Linköping University, Department of Computer and Information Science, Database and information techniques. Linköping University, The Institute of Technology.
    Ontology-based integration for bioinformatics2005In: Proceedings of the VLDB Workshop on Ontologies-bases techniques for DataBases and Information Systems - ODBIS, 2005, p. 55-58Conference paper (Refereed)
    Abstract [en]

    Information integration systems support researchers in bioinformatics to retrieve data from multiple biological data sources. In this paper we argue that the current approaches should be enhanced by ontological knowledge. We identify the dierent types of ontological knowledge that are available on the Web and propose an approach to use this knowledge to support integrated access to multiple biological data sources. We also show that current ontology-based integration approaches only cover parts of our approach.

     

  • 12.
    Jakoniené, Vaida
    et al.
    Linköping University, Department of Computer and Information Science, Database and information techniques. Linköping University, The Institute of Technology.
    Lambrix, Patrick
    Linköping University, Department of Computer and Information Science, Database and information techniques. Linköping University, The Institute of Technology.
    Tool for Evaluating Strategies for Grouping of Biological Data2007In: Journal of Integrative Bioinformatics, ISSN 1613-4516, Vol. 4, no 3Article in journal (Refereed)
    Abstract [en]

    During the last decade an enormous amount of biological data has been generated and techniques and tools to analyze this data have been developed. Many of these tools use some form of grouping and are used in, for instance, data integration, data cleaning, prediction of protein functionality, and correlation of genes based on microarray data. A number of aspects influence the quality of the grouping results: the data sources, the grouping attributes and the algorithms implementing the grouping procedure. Many methods exist, but it is often not clear which methods perform best for which grouping tasks. The study of the properties, and the evaluation and the comparison of the different aspects that influence the quality of the grouping results, would give us valuable insight in how the grouping procedures could be used in the best way. It would also lead to recommendations on how to improve the current procedures and develop new procedures. To be able to perform such studies and evaluations we need environments that allow us to compare and evaluate different grouping strategies. In this paper we present a framework, KitEGA, for such an environment, and present its current prototype implementation. We illustrate its use by comparing grouping strategies for classifying proteins regarding biological function and isozymes.

  • 13.
    Jakoniené, Vaida
    et al.
    Linköping University, Department of Computer and Information Science, Database and information techniques. Linköping University, The Institute of Technology.
    Rundqvist, David
    Linköping University, The Institute of Technology. Linköping University, Department of Computer and Information Science, Database and information techniques.
    Lambrix, Patrick
    Linköping University, Department of Computer and Information Science, Database and information techniques. Linköping University, The Institute of Technology.
    A Method for Similarity-Based Grouping of Biological Data2006In: DILS: International Workshop on Data Integration in the Life Sciences Data Integration in the Life Sciences Third International Workshop, DILS 2006, Hinxton, UK, July 20-22, 2006. Proceedings / [ed] Ulf Leser, Felix Naumann, Barbara Eckman, Springer Berlin/Heidelberg, 2006, p. 136-151Conference paper (Refereed)
    Abstract [en]

    Similarity-based grouping of data entries in one or more data sources is a task underlying many different data management tasks, such as, structuring search results, removal of redundancy in databases and data integration. Similarity-based grouping of data entries is not a trivial task in the context of life science data sources as the stored data is complex, highly correlated and represented at different levels of granularity. The contribution of this paper is two-fold. 1) We propose a method for similarity-based grouping and 2) we show results from test cases. As the main steps the method contains specification of grouping rules, pairwise grouping between entries, actual grouping of similar entries, and evaluation and analysis of the results. Often, different strategies can be used in the different steps. The method enables exploration of the influence of the choices and supports evaluation of the results with respect to given classifications. The grouping method is illustrated by test cases based on different strategies and classifications. The results show the complexity of the similarity-based grouping tasks and give deeper insights in the selected grouping tasks, the analyzed data source, and the influence of different strategies on the results.

  • 14.
    Jakonienė, Vaida
    Linköping University, Department of Computer and Information Science, IISLAB - Laboratory for Intelligent Information Systems. Linköping University, The Institute of Technology.
    Integration of Biological Data2006Doctoral thesis, comprehensive summary (Other academic)
    Abstract [en]

    Data integration is an important procedure underlying many research tasks in the life sciences, as often multiple data sources have to be accessed to collect the relevant data. The data sources vary in content, data format, and access methods, which often vastly complicates the data retrieval process. As a result, the task of retrieving data requires a great deal of effort and expertise on the part of the user. To alleviate these difficulties, various information integration systems have been proposed in the area. However, a number of issues remain unsolved and new integration solutions are needed.

    The work presented in this thesis considers data integration at three different levels. 1) Integration of biological data sources deals with integrating multiple data sources from an information integration system point of view. We study properties of biological data sources and existing integration systems. Based on the study, we formulate requirements for systems integrating biological data sources. Then, we define a query language that supports queries commonly used by biologists. Also, we propose a high-level architecture for an information integration system that meets a selected set of requirements and that supports the specified query language. 2) Integration of ontologies deals with finding overlapping information between ontologies. We develop and evaluate algorithms that use life science literature and take the structure of the ontologies into account. 3) Grouping of biological data entries deals with organizing data entries into groups based on the computation of similarity values between the data entries. We propose a method that covers the main steps and components involved in similarity-based grouping procedures. The applicability of the method is illustrated by a number of test cases. Further, we develop an environment that supports comparison and evaluation of different grouping strategies.

    The work is supported by the implementation of: 1) a prototype for a system integrating biological data sources, called BioTRIFU, 2) algorithms for ontology alignment, and 3) an environment for evaluating strategies for similarity-based grouping of biological data, called KitEGA.

    List of papers
    1. Towards transparent access to multiple biological databanks
    Open this publication in new window or tab >>Towards transparent access to multiple biological databanks
    2003 (English)In: Proceedings of the first Asia-Pacific Bioinformatics Conference, Adelaide, Australia, 2003, Vol. 33, p. 53-60Conference paper, Published paper (Refereed)
    Abstract [en]

    Nowadays, biologists use a number of large biological databanks to find relevant information for their research. Users of these databanks face a number of problems. One problem is that users are required to have good knowledge about the contents, implementations and conceptual models of many databanks to be able to ask precise and relevant questions. Further, the terminology that is used in the different databanks may be different. Also, when asking complex queries to multiple databanks, users need to construct a query plan on their own possibly leading to poor performance or not even obtaining results. To alleviate these problems we define an architecture for systems that deal with these problems by allowing for a transparent and integrated way to query the multiple sources. The contribution of this paper is threefold. First, we describe a study of current biological databanks. Then, we propose a base query language that contains operators that should be present in any query language for biological databanks. Further, we present an architecture for a system supporting such a language and providing integrated access to the highly distributed and heterogeneous environment of biological databanks.

    National Category
    Engineering and Technology
    Identifiers
    urn:nbn:se:liu:diva-14032 (URN)
    Available from: 2006-09-28 Created: 2006-09-28 Last updated: 2015-02-18
    2. Information integration systems for biological data source requirements and opportunities
    Open this publication in new window or tab >>Information integration systems for biological data source requirements and opportunities
    2006 (English)Report (Other (popular science, discussion, etc.))
    National Category
    Computer Sciences
    Identifiers
    urn:nbn:se:liu:diva-14033 (URN)
    Available from: 2006-09-28 Created: 2006-09-28 Last updated: 2018-01-13
    3. Ontology-based integration for bioinformatics
    Open this publication in new window or tab >>Ontology-based integration for bioinformatics
    2005 (English)In: Proceedings of the VLDB Workshop on Ontologies-bases techniques for DataBases and Information Systems - ODBIS, 2005, p. 55-58Conference paper, Published paper (Refereed)
    Abstract [en]

    Information integration systems support researchers in bioinformatics to retrieve data from multiple biological data sources. In this paper we argue that the current approaches should be enhanced by ontological knowledge. We identify the dierent types of ontological knowledge that are available on the Web and propose an approach to use this knowledge to support integrated access to multiple biological data sources. We also show that current ontology-based integration approaches only cover parts of our approach.

     

    National Category
    Engineering and Technology
    Identifiers
    urn:nbn:se:liu:diva-14034 (URN)
    Conference
    Workshop on Ontologies-bases techniques for DataBases and Information Systems - ODBIS
    Available from: 2006-09-28 Created: 2006-09-28 Last updated: 2015-02-18
    4. Alignment of Biomedical Ontologies using Life Science Literature
    Open this publication in new window or tab >>Alignment of Biomedical Ontologies using Life Science Literature
    Show others...
    2006 (English)In: KDLL: International Workshop on Knowledge Discovery in Life Science LIterature Knowledge Discovery in Life Science Literature PAKDD 2006 International Workshop, KDLL 2006, Singapore, April 9, 2006. Proceedings / [ed] Eric G. Bremer, Jörg Hakenberg, Eui-Hong (Sam) Han, Daniel Berrar and Werner Dubitzky, Berlin/Heidelberg: Springer, 2006, p. 1-17Conference paper, Published paper (Refereed)
    Abstract [en]

    This book constitutes the refereed proceedings of the International Workshop on Knowledge Discovery in Life Science Literature, KDLL 2006, held in Singapore in conjunction with the 10th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2006).

    The 12 revised full papers presented together with two invited talks were carefully reviewed and selected for inclusion in the book. The papers cover all topics of knowledge discovery in life science data such as text mining, identification and retrieval of documents, passage retrieval, co-reference resolution, extraction of life science entities or relationships from large collections, automated characterization of biological, biomedical and biotechnological entities and processes, extraction and characterization of more complex patterns and interaction networks, automated generation of text summaries, automated construction, expansion and curation of ontologies for different domains, and construction of controlled vocabularies.

    Place, publisher, year, edition, pages
    Berlin/Heidelberg: Springer, 2006
    Series
    Lecture Notes in Computer Science, ISSN 0302-9743, E-ISSN 1611-3349 ; 3886
    National Category
    Computer Sciences
    Identifiers
    urn:nbn:se:liu:diva-14035 (URN)10.1007/11683568_1 (DOI)000237198800001 ()978-3-540-32809-4 (ISBN)3-540-32809-2 (ISBN)
    Conference
    KDLL: International Workshop on Knowledge Discovery in Life Science LIterature Knowledge Discovery in Life Science Literature PAKDD 2006 International Workshop, KDLL 2006, Singapore, April 9, 2006
    Available from: 2006-09-28 Created: 2006-09-28 Last updated: 2018-11-27Bibliographically approved
    5. A Method for Similarity-Based Grouping of Biological Data
    Open this publication in new window or tab >>A Method for Similarity-Based Grouping of Biological Data
    2006 (English)In: DILS: International Workshop on Data Integration in the Life Sciences Data Integration in the Life Sciences Third International Workshop, DILS 2006, Hinxton, UK, July 20-22, 2006. Proceedings / [ed] Ulf Leser, Felix Naumann, Barbara Eckman, Springer Berlin/Heidelberg, 2006, p. 136-151Conference paper, Published paper (Refereed)
    Abstract [en]

    Similarity-based grouping of data entries in one or more data sources is a task underlying many different data management tasks, such as, structuring search results, removal of redundancy in databases and data integration. Similarity-based grouping of data entries is not a trivial task in the context of life science data sources as the stored data is complex, highly correlated and represented at different levels of granularity. The contribution of this paper is two-fold. 1) We propose a method for similarity-based grouping and 2) we show results from test cases. As the main steps the method contains specification of grouping rules, pairwise grouping between entries, actual grouping of similar entries, and evaluation and analysis of the results. Often, different strategies can be used in the different steps. The method enables exploration of the influence of the choices and supports evaluation of the results with respect to given classifications. The grouping method is illustrated by test cases based on different strategies and classifications. The results show the complexity of the similarity-based grouping tasks and give deeper insights in the selected grouping tasks, the analyzed data source, and the influence of different strategies on the results.

    Place, publisher, year, edition, pages
    Springer Berlin/Heidelberg, 2006
    Series
    Lecture Notes in Computer Science, ISSN 0302-9743, E-ISSN 1611-3349 ; 4075
    Series
    Lecture Notes in Bioinformatics ; 4075
    National Category
    Engineering and Technology
    Identifiers
    urn:nbn:se:liu:diva-14036 (URN)10.1007/11799511_13 (DOI)000239622300011 ()978-3-540-36595-2 (ISBN)978-3-540-36593-8 (ISBN)
    Conference
    DILS: International Workshop on Data Integration in the Life Sciences Data Integration in the Life Sciences Third International Workshop, DILS 2006, Hinxton, UK, July 20-22, 2006.
    Available from: 2006-09-28 Created: 2006-09-28 Last updated: 2018-11-27Bibliographically approved
    6. Tool for Evaluating Strategies for Grouping of Biological Data
    Open this publication in new window or tab >>Tool for Evaluating Strategies for Grouping of Biological Data
    2007 (English)In: Journal of Integrative Bioinformatics, ISSN 1613-4516, Vol. 4, no 3Article in journal (Refereed) Published
    Abstract [en]

    During the last decade an enormous amount of biological data has been generated and techniques and tools to analyze this data have been developed. Many of these tools use some form of grouping and are used in, for instance, data integration, data cleaning, prediction of protein functionality, and correlation of genes based on microarray data. A number of aspects influence the quality of the grouping results: the data sources, the grouping attributes and the algorithms implementing the grouping procedure. Many methods exist, but it is often not clear which methods perform best for which grouping tasks. The study of the properties, and the evaluation and the comparison of the different aspects that influence the quality of the grouping results, would give us valuable insight in how the grouping procedures could be used in the best way. It would also lead to recommendations on how to improve the current procedures and develop new procedures. To be able to perform such studies and evaluations we need environments that allow us to compare and evaluate different grouping strategies. In this paper we present a framework, KitEGA, for such an environment, and present its current prototype implementation. We illustrate its use by comparing grouping strategies for classifying proteins regarding biological function and isozymes.

    National Category
    Engineering and Technology Computer Sciences Bioinformatics (Computational Biology)
    Identifiers
    urn:nbn:se:liu:diva-14037 (URN)10.2390/biecoll-jib-2007-83 (DOI)
    Available from: 2006-09-28 Created: 2006-09-28 Last updated: 2018-01-13
  • 15.
    Lambrix, Patrick
    et al.
    Linköping University, Department of Computer and Information Science, IISLAB - Laboratory for Intelligent Information Systems. Linköping University, The Institute of Technology.
    Jakoniene, Vaida
    Linköping University, Department of Computer and Information Science, IISLAB - Laboratory for Intelligent Information Systems. Linköping University, The Institute of Technology.
    A databank knowledge base for the integration of biological databanks2002In: Bioinformatics, 2002Conference paper (Other academic)
  • 16.
    Lambrix, Patrick
    et al.
    Linköping University, Department of Computer and Information Science, IISLAB. Linköping University, The Institute of Technology.
    Jakoniené, Vaida
    Linköping University, Department of Computer and Information Science, IISLAB. Linköping University, The Institute of Technology.
    Towards transparent access to multiple biological databanks2003In: Proceedings of the first Asia-Pacific Bioinformatics Conference, Adelaide, Australia, 2003, Vol. 33, p. 53-60Conference paper (Refereed)
    Abstract [en]

    Nowadays, biologists use a number of large biological databanks to find relevant information for their research. Users of these databanks face a number of problems. One problem is that users are required to have good knowledge about the contents, implementations and conceptual models of many databanks to be able to ask precise and relevant questions. Further, the terminology that is used in the different databanks may be different. Also, when asking complex queries to multiple databanks, users need to construct a query plan on their own possibly leading to poor performance or not even obtaining results. To alleviate these problems we define an architecture for systems that deal with these problems by allowing for a transparent and integrated way to query the multiple sources. The contribution of this paper is threefold. First, we describe a study of current biological databanks. Then, we propose a base query language that contains operators that should be present in any query language for biological databanks. Further, we present an architecture for a system supporting such a language and providing integrated access to the highly distributed and heterogeneous environment of biological databanks.

  • 17.
    Lambrix, Patrick
    et al.
    Linköping University, Department of Computer and Information Science, Database and information techniques. Linköping University, The Institute of Technology.
    Tan, He
    Linköping University, Department of Computer and Information Science, Database and information techniques. Linköping University, The Institute of Technology.
    Jakoniené, Vaida
    Linköping University, Department of Computer and Information Science, Database and information techniques. Linköping University, The Institute of Technology.
    Strömbäck, Lena
    Linköping University, Department of Computer and Information Science, Database and information techniques. Linköping University, The Institute of Technology.
    Biological Ontologies2007In: Semantic Web: Revolutionizing Knowledge Discovery in the Life Sciences / [ed] Christopher J.O. Baker and Kei-Hoi Cheung, Springer , 2007, p. 85-99Chapter in book (Refereed)
    Abstract [en]

    Biological ontologies define the basic terms and relations in biological domains and are being used among others, as community reference, as the basis for interoperability between systems, and for search, integration, and exchange of biological data. In this chapter we present examples of biological ontologies and ontology-based knowledge, show how biological ontologies are used and discuss some important issues in ontology engineering.

  • 18.
    Strömbäck, Lena
    et al.
    Linköping University, Department of Computer and Information Science, Database and information techniques. Linköping University, The Institute of Technology.
    Jakoniené, Vaida
    Linköping University, Department of Computer and Information Science, Database and information techniques. Linköping University, The Institute of Technology.
    Tan, He
    Linköping University, Department of Computer and Information Science, Database and information techniques. Linköping University, The Institute of Technology.
    Lambrix, Patrick
    Linköping University, Department of Computer and Information Science, Database and information techniques. Linköping University, The Institute of Technology.
    Representing, storing and accessing molecular interaction data: a review of models and tools2006In: Briefings in Bioinformatics, ISSN 1467-5463, E-ISSN 1477-4054, Vol. 7, no 4, p. 331-338Article in journal (Refereed)
    Abstract [en]

    One important aim within systems biology is to integrate disparate pieces of information, leading to discovery of higher-level knowledge about important functionality within living organisms. This makes standards for representation of data and technology for exchange and integration of data important key points for development within the area. In this article, we focus on the recent developments within the field. We compare the recent updates to the three standard representations for exchange of data SBML, PSI MI and BioPAX. In addition, we give an overview of available tools for these three standards and a discussion on how these developments support possibilities for data exchange and integration.

  • 19.
    Tan, He
    et al.
    Linköping University, Department of Computer and Information Science, Database and information techniques. Linköping University, The Institute of Technology.
    Jakoniené, Vaida
    Linköping University, Department of Computer and Information Science, Database and information techniques. Linköping University, The Institute of Technology.
    Lambrix, Patrick
    Linköping University, Department of Computer and Information Science, Database and information techniques. Linköping University, The Institute of Technology.
    Åberg, Johan
    Linköping University, Department of Computer and Information Science, Database and information techniques. Linköping University, The Institute of Technology.
    Shahmehri, Nahid
    Linköping University, Department of Computer and Information Science, Database and information techniques. Linköping University, The Institute of Technology.
    Alignment of Biomedical Ontologies using Life Science Literature2006In: KDLL: International Workshop on Knowledge Discovery in Life Science LIterature Knowledge Discovery in Life Science Literature PAKDD 2006 International Workshop, KDLL 2006, Singapore, April 9, 2006. Proceedings / [ed] Eric G. Bremer, Jörg Hakenberg, Eui-Hong (Sam) Han, Daniel Berrar and Werner Dubitzky, Berlin/Heidelberg: Springer, 2006, p. 1-17Conference paper (Refereed)
    Abstract [en]

    This book constitutes the refereed proceedings of the International Workshop on Knowledge Discovery in Life Science Literature, KDLL 2006, held in Singapore in conjunction with the 10th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2006).

    The 12 revised full papers presented together with two invited talks were carefully reviewed and selected for inclusion in the book. The papers cover all topics of knowledge discovery in life science data such as text mining, identification and retrieval of documents, passage retrieval, co-reference resolution, extraction of life science entities or relationships from large collections, automated characterization of biological, biomedical and biotechnological entities and processes, extraction and characterization of more complex patterns and interaction networks, automated generation of text summaries, automated construction, expansion and curation of ontologies for different domains, and construction of controlled vocabularies.

1 - 19 of 19
CiteExportLink to result list
Permanent link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • oxford
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf