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Data Integration in the Life Sciences

7th International Conference, DILS 2010, Gothenburg, Sweden, August 25-27, 2010. Proceedings

  • Conference proceedings
  • © 2010

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Editors:
  1. Patrick Lambrix

    1. Department of Computer and Information Science, Linkö**s universitet, Linkö**, Sweden

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  2. Graham Kemp

    1. Computer Science and Engineering, Chalmers University of Technology and University of Gothenburg, Gothenburg,, Sweden

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    You can also search for this editor in PubMed   Google Scholar

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Part of the book series: Lecture Notes in Computer Science (LNCS, volume 6254)

Part of the book sub series: Lecture Notes in Bioinformatics (LNBI)

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Conference proceedings info: DILS 2010.

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About this book

The development and increasingly widespread deployment of high-throughput experimental methods in the life sciences is giving rise to numerous large, c- plex and valuable data resources. This foundation of experimental data und- pins the systematic study of organismsand diseases, which increasinglydepends on the development of models of biological systems. The development of these models often requires integration of diverse experimental data resources; once constructed, the models themselves become data and present new integration challenges for tasks such as interpretation, validation and comparison. The Data Integration in the Life Sciences (DILS) Conference 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. DILS 2010 was the seventh event in the series and was held in Goth- burg, Sweden during August 25–27, 2010. The associated proceedings contain 14 peer-reviewed papers and 2 invited papers. The sessions addressed ontology engineering, and in particular, evolution, matching and debugging of ontologies, akeycomponentforsemanticintegration;Web servicesasanimportanttechn- ogy for data integration in the life sciences; data and text mining techniques for discovering and recognizing biomedical entities and relationships between these entities; and information management, introducing data integration solutions for di?erent types of applications related to cancer, systems biology and - croarray experimental data, and an approach for integrating ranked data in the life sciences.

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Table of contents (16 papers)

  1. Front Matter

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  2. Invited Talks

    1. Provenance Management for Data Exploration

      • Juliana Freire
      Pages 1-2

    2. High-Performance Systems for in Silico Microscopy Imaging Studies

      • Fusheng Wang, Tahsin Kurc, Patrick Widener, Tony Pan, Jun Kong, Lee Cooper et al.
      Pages 3-18

  3. Ontology Engineering

    1. Discovering Evolving Regions in Life Science Ontologies

      • Michael Hartung, Anika Gross, Toralf Kirsten, Erhard Rahm
      Pages 19-34

    2. On Matching Large Life Science Ontologies in Parallel

      • Anika Gross, Michael Hartung, Toralf Kirsten, Erhard Rahm
      Pages 35-49

    3. A System for Debugging Missing Is-a Structure in Networked Ontologies

      • Qiang Liu, Patrick Lambrix
      Pages 50-57

  4. Web Services

    1. On the Secure Sharing and Aggregation of Data to Support Systems Biology Research

      • Andrew Simpson, Mark Slaymaker, David Gavaghan
      Pages 58-73

    2. Hel** Biologists Effectively Build Workflows, without Programming

      • Paul M. K. Gordon, Ken Barker, Christoph W. Sensen
      Pages 74-89

    3. A Data Warehouse Approach to Semantic Integration of Pseudomonas Data

      • Kamar Marrakchi, Abdelaali Briache, Amine Kerzazi, Ismael Navas-Delgado, José Francisco Aldana-Montes, Mohamed Ettayebi et al.
      Pages 90-105

  5. Data Mining and Text Mining

    1. The Cinderella of Biological Data Integration: Addressing Some of the Challenges of Entity and Relationship Mining from Patent Sources

      • Ithipol Suriyawongkul, Christopher Southan, Sorel Muresan
      Pages 106-121

    2. Algorithm for Grounding Mutation Mentions from Text to Protein Sequences

      • Jonas Bergman Laurila, Rajaraman Kanagasabai, Christopher J. O. Baker
      Pages 122-131

    3. Handling Missing Features with Boosting Algorithms for Protein–Protein Interaction Prediction

      • Fabrizio Smeraldi, Michael Defoin-Platel, Mansoor Saqi
      Pages 132-147

    4. Instance Discovery and Schema Matching with Applications to Biological Deep Web Data Integration

      • Tantan Liu, Fan Wang, Gagan Agrawal
      Pages 148-163

  6. Information Management

    1. Integrative Information Management for Systems Biology

      • Neil Swainston, Daniel Jameson, Peter Li, Irena Spasic, Pedro Mendes, Norman W. Paton
      Pages 164-178

    2. An Integration Architecture Designed to Deal with the Issues of Biological Scope, Scale and Complexity

      • Hector Rovira, Sarah Killcoyne, Ilya Shmulevich, John Boyle
      Pages 179-191

    3. Quality Assessment of MAGE-ML Genomic Datasets Using DescribeX

      • Lorena Etcheverry, Shahan Khatchadourian, Mariano Consens
      Pages 192-206

    4. Search Computing: Integrating Ranked Data in the Life Sciences

      • Marco Masseroli, Norman W. Paton, Giorgio Ghisalberti
      Pages 207-214

  7. Back Matter

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  1. Data Integration in the Life Sciences

Editors and Affiliations

  • Department of Computer and Information Science, Linkö**s universitet, Linkö**, Sweden

    Patrick Lambrix

  • Computer Science and Engineering, Chalmers University of Technology and University of Gothenburg, Gothenburg,, Sweden

    Graham Kemp

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