Data Integration in the Life Sciences 7th International Conference, DILS 2010, Gothenburg, Sweden, August 25-27, 2010. Proceedings /

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 increasinglyd...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Autor Corporativo: SpringerLink (Online service)
Otros Autores: Lambrix, Patrick (Editor ), Kemp, Graham (Editor )
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer, 2010.
Edición:1st ed. 2010.
Colección:Lecture Notes in Bioinformatics, 6254
Temas:
Life sciences.
Computer engineering.
Computer networks .
Application software.
Artificial intelligence.
Information storage and retrieval systems.
Life Sciences.
Computer Engineering and Networks.
Computer Communication Networks.
Computer and Information Systems Applications.
Artificial Intelligence.
Information Storage and Retrieval.
Acceso en línea:Texto Completo
Tabla de Contenidos:
  • Invited Talks
  • Provenance Management for Data Exploration
  • High-Performance Systems for in Silico Microscopy Imaging Studies
  • Ontology Engineering
  • Discovering Evolving Regions in Life Science Ontologies
  • On Matching Large Life Science Ontologies in Parallel
  • A System for Debugging Missing Is-a Structure in Networked Ontologies
  • Web Services
  • On the Secure Sharing and Aggregation of Data to Support Systems Biology Research
  • Helping Biologists Effectively Build Workflows, without Programming
  • A Data Warehouse Approach to Semantic Integration of Pseudomonas Data
  • Data Mining and Text Mining
  • The Cinderella of Biological Data Integration: Addressing Some of the Challenges of Entity and Relationship Mining from Patent Sources
  • Algorithm for Grounding Mutation Mentions from Text to Protein Sequences
  • Handling Missing Features with Boosting Algorithms for Protein-Protein Interaction Prediction
  • Instance Discovery and Schema Matching with Applications to Biological Deep Web Data Integration
  • Information Management
  • Integrative Information Management for Systems Biology
  • An Integration Architecture Designed to Deal with the Issues of Biological Scope, Scale and Complexity
  • Quality Assessment of MAGE-ML Genomic Datasets Using DescribeX
  • Search Computing: Integrating Ranked Data in the Life Sciences.

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