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Information integration systems for biological data source requirements and opportunities
Linköping University, Department of Computer and Information Science, Database and information techniques. Linköping University, The Institute of Technology.
Linköping University, Department of Computer and Information Science, Database and information techniques. Linköping University, The Institute of Technology. (IDA/ADIT)ORCID iD: 0000-0002-9084-0470
2006 (English)Report (Other (popular science, discussion, etc.))
Place, publisher, year, edition, pages
2006.
National Category
Computer Science
Identifiers
URN: urn:nbn:se:liu:diva-14033OAI: oai:DiVA.org:liu-14033DiVA: diva2:22495
Available from: 2006-09-28 Created: 2006-09-28 Last updated: 2015-02-18
In thesis
1. Integration of Biological Data
Open this publication in new window or tab >>Integration of Biological Data
2006 (English)Doctoral 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.

Place, publisher, year, edition, pages
Institutionen för datavetenskap, 2006. 20 p.
Series
Linköping Studies in Science and Technology. Dissertations, ISSN 0345-7524 ; 1035
Keyword
Datalogi, integration, grouping, databases, ontologies, biological data, ioinformatics, KitEGA, Datalogi
National Category
Engineering and Technology
Identifiers
urn:nbn:se:liu:diva-7484 (URN)91-85523-28-3 (ISBN)
Public defence
2006-09-25, Visionen, Hus B, Campus Valla, Linköpings universitet, Linköping, 13:15 (English)
Opponent
Supervisors
Available from: 2006-09-28 Created: 2006-09-28 Last updated: 2017-08-15Bibliographically approved

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