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Data Integration in the Life Sciences

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Cover of 'Data Integration in the Life Sciences'

Table of Contents

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    Book Overview
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    Chapter 1 Challenges in Biological Data Integration in the Post-genome Sequence Era
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    Chapter 2 Curated Databases
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    Chapter 3 A User-Centric Framework for Accessing Biological Sources and Tools
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    Chapter 4 BioLog: A Browser Based Collaboration and Resource Navigation Assistant for BioMedical Researchers
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    Chapter 5 Learning Layouts of Biological Datasets Semi-automatically
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    Chapter 6 Factors Affecting Ontology Development in Ecology
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    Chapter 7 Querying Ontologies in Relational Database Systems
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    Chapter 8 Scientific Names Are Ambiguous as Identifiers for Biological Taxa: Their Context and Definition Are Required for Accurate Data Integration
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    Chapter 9 The Multiple Roles of Ontologies in the BioMediator Data Integration System
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    Chapter 10 Integrating Heterogeneous Microarray Data Sources Using Correlation Signatures
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    Chapter 11 Knowledge-Based Integrative Framework for Hypothesis Formation in Biochemical Networks
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    Chapter 12 Semantic Correspondence in Federated Life Science Data Integration Systems
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    Chapter 13 Assigning Unique Keys to Chemical Compounds for Data Integration: Some Interesting Counter Examples
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    Chapter 14 Integrating and Warehousing Liver Gene Expression Data and Related Biomedical Resources in GEDAW
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    Chapter 15 Information Integration and Knowledge Acquisition from Semantically Heterogeneous Biological Data Sources
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    Chapter 16 Cluster Based Integration of Heterogeneous Biological Databases Using the AutoMed Toolkit
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    Chapter 17 Hybrid Integration of Molecular-Biological Annotation Data
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    Chapter 18 Setup and Annotation of Metabolomic Experiments by Integrating Biological and Mass Spectrometric Metadata
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    Chapter 19 Performance-Oriented Privacy-Preserving Data Integration
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    Chapter 20 Building a Generic Platform for Medical Screening Applications Based on Domain Specific Modeling and Process Orientation
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    Chapter 21 Automatic Generation of Data Types for Classification of Deep Web Sources
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    Chapter 22 BioNavigation: Selecting Optimum Paths Through Biological Resources to Evaluate Ontological Navigational Queries
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    Chapter 23 Support for BioIndexing in BLASTgres
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    Chapter 24 An Environment to Define and Execute In-Silico Workflows Using Web Services
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    Chapter 25 Web Service Mining for Biological Pathway Discovery
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    Chapter 26 SemanticBio: Building Conceptual Scientific Workflows over Web Services
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    Chapter 27 PLATCOM: Current Status and Plan for the Next Stages
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    Chapter 28 SOAP API for Integrating Biological Interaction Databases
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    Chapter 29 Collaborative Curation of Data from Bio-medical Texts and Abstracts and Its integration
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    Chapter 30 Towards an Ontology Based Visual Query System
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    Chapter 31 Data Integration in the Biomedical Informatics Research Network (BIRN)
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    Chapter 32 Data Integration and Workflow Solutions for Ecology
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    Chapter 33 Eco-Informatics for Decision Makers Advancing a Research Agenda
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    Chapter 34 An Architecture and Application for Integrating Curation Data at the Residue Level for Proteins
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    Chapter 35 The Biozon System for Complex Analysis of Heterogeneous Interrelated Biological Data and Discovery of Emergent Structures
Attention for Chapter 30: Towards an Ontology Based Visual Query System
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Chapter title
Towards an Ontology Based Visual Query System
Chapter number 30
Book title
Data Integration in the Life Sciences
Published by
Springer, Berlin, Heidelberg, July 2005
DOI 10.1007/11530084_30
Book ISBNs
978-3-54-027967-9, 978-3-54-031879-8
Authors

Serguei Krivov, Ferdinando Villa

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 2 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 2 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 1 50%
Researcher 1 50%
Student > Doctoral Student 1 50%
Readers by discipline Count As %
Agricultural and Biological Sciences 2 100%
Computer Science 1 50%