Data Integration in the Life Sciences 6th International Workshop, DILS 2009, Manchester, UK, July 20-22, 2009, Proceedings / [electronic resource] :
edited by Norman W. Paton, Paolo Missier, Cornelia Hedeler.
- 1st ed. 2009.
- X, 221 p. online resource.
- Lecture Notes in Bioinformatics, 5647 2366-6331 ; .
- Lecture Notes in Bioinformatics, 5647 .
Keynote Presentations -- Data Integration and Exchange for Scientific Collaboration -- Data Integration and Semantic Enrichment of Systems Biology Models and Simulations -- Graph-Based Modelling and Integration -- Linking Life Sciences Data Using Graph-Based Mapping -- Integration of Full-Coverage Probabilistic Functional Networks with Relevance to Specific Biological Processes -- OpenFlyData: The Way to Go for Biological Data Integration -- Annotation -- On the Reachability of Trustworthy Information from Integrated Exploratory Biological Queries -- Estimating the Quality of Ontology-Based Annotations by Considering Evolutionary Changes -- Integration and Mining of Genomic Annotations: Experiences and Perspectives in GFINDer Data Warehousing -- Structure Inference -- Site-Wide Wrapper Induction for Life Science Deep Web Databases -- An Adaptive Combination of Matchers: Application to the Mapping of Biological Ontologies for Genome Annotation -- Slicing through the Scientific Literature -- Data and Work Flows -- Exploiting Parallelism to Accelerate Keyword Search on Deep-Web Sources -- A Visual Interface for on-the-fly Biological Database Integration and Workflow Design Using VizBuilder -- EpiC: A Resource for Integrating Information and Analyses to Enable Selection of Epitopes for Antibody Based Experiments -- Data Integration for Systems Biology -- Design and Architecture of Web Services for Simulation of Biochemical Systems -- An Integration and Analysis Pipeline for Systems Biology in Crop Plant Metabolism -- Towards Enhanced Retrieval of Biological Models through Annotation-Based Ranking.
Data integration in the life sciences continues to be important but challe- ing. The ongoing development of new experimental methods gives rise to an increasingly wide range of data sets, which in turn must be combined to allow more integrative views of biological systems. Indeed, the growing prominence of systems biology, where mathematical models characterize behaviors observed in experiments of di?erent types, emphasizes the importance of data integration to the life sciences. In this context, the representation of models of biological behavior as data in turn gives rise to challenges relating to provenance, data quality, annotation, etc., all of which are associated with signi?cant research activities within computer science. The Data Integration in the Life Sciences (DILS) Workshop Series brings together data and knowledge management researchers from the computer s- ence research community with bioinformaticians and computational biologists, to improve the understanding of how emerging data integration techniques can address requirements identi?ed in the life sciences.
9783642028793
10.1007/978-3-642-02879-3 doi
Medical sciences.
Life sciences.
Data mining.
Application software.
Database management.
Bioinformatics.
Health Sciences.
Life Sciences.
Data Mining and Knowledge Discovery.
Computer and Information Systems Applications.
Database Management.
Computational and Systems Biology.
R1
610
Keynote Presentations -- Data Integration and Exchange for Scientific Collaboration -- Data Integration and Semantic Enrichment of Systems Biology Models and Simulations -- Graph-Based Modelling and Integration -- Linking Life Sciences Data Using Graph-Based Mapping -- Integration of Full-Coverage Probabilistic Functional Networks with Relevance to Specific Biological Processes -- OpenFlyData: The Way to Go for Biological Data Integration -- Annotation -- On the Reachability of Trustworthy Information from Integrated Exploratory Biological Queries -- Estimating the Quality of Ontology-Based Annotations by Considering Evolutionary Changes -- Integration and Mining of Genomic Annotations: Experiences and Perspectives in GFINDer Data Warehousing -- Structure Inference -- Site-Wide Wrapper Induction for Life Science Deep Web Databases -- An Adaptive Combination of Matchers: Application to the Mapping of Biological Ontologies for Genome Annotation -- Slicing through the Scientific Literature -- Data and Work Flows -- Exploiting Parallelism to Accelerate Keyword Search on Deep-Web Sources -- A Visual Interface for on-the-fly Biological Database Integration and Workflow Design Using VizBuilder -- EpiC: A Resource for Integrating Information and Analyses to Enable Selection of Epitopes for Antibody Based Experiments -- Data Integration for Systems Biology -- Design and Architecture of Web Services for Simulation of Biochemical Systems -- An Integration and Analysis Pipeline for Systems Biology in Crop Plant Metabolism -- Towards Enhanced Retrieval of Biological Models through Annotation-Based Ranking.
Data integration in the life sciences continues to be important but challe- ing. The ongoing development of new experimental methods gives rise to an increasingly wide range of data sets, which in turn must be combined to allow more integrative views of biological systems. Indeed, the growing prominence of systems biology, where mathematical models characterize behaviors observed in experiments of di?erent types, emphasizes the importance of data integration to the life sciences. In this context, the representation of models of biological behavior as data in turn gives rise to challenges relating to provenance, data quality, annotation, etc., all of which are associated with signi?cant research activities within computer science. The Data Integration in the Life Sciences (DILS) Workshop Series brings together data and knowledge management researchers from the computer s- ence research community with bioinformaticians and computational biologists, to improve the understanding of how emerging data integration techniques can address requirements identi?ed in the life sciences.
9783642028793
10.1007/978-3-642-02879-3 doi
Medical sciences.
Life sciences.
Data mining.
Application software.
Database management.
Bioinformatics.
Health Sciences.
Life Sciences.
Data Mining and Knowledge Discovery.
Computer and Information Systems Applications.
Database Management.
Computational and Systems Biology.
R1
610