LEADER 05453nam 22006614a 450 001 9910784532303321 005 20200520144314.0 010 $a1-281-07813-1 010 $a9786611078133 010 $a0-08-052798-1 035 $a(CKB)1000000000384503 035 $a(EBL)317252 035 $a(OCoLC)476110958 035 $a(SSID)ssj0000111874 035 $a(PQKBManifestationID)11131001 035 $a(PQKBTitleCode)TC0000111874 035 $a(PQKBWorkID)10080985 035 $a(PQKB)11302534 035 $a(Au-PeEL)EBL317252 035 $a(CaPaEBR)ebr10206738 035 $a(CaONFJC)MIL107813 035 $a(CaSebORM)9781558608290 035 $a(MiAaPQ)EBC317252 035 $a(EXLCZ)991000000000384503 100 $a20030224d2003 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 00$aBioinformatics$b[electronic resource] $emanaging scientific data /$fedited by Zoe? Lacroix and Terence Critchlow 205 $a1st edition 210 $aSan Francisco, CA $cMorgan Kaufmann Publishers$dc2003 215 $a1 online resource (465 p.) 225 1 $aThe Morgan Kaufmann series in multimedia information and systems 300 $aDescription based upon print version of record. 311 $a1-55860-829-X 320 $aIncludes bibliographical references and index. 327 $aCover; Bioinformatics: Managing Scientific Data; Copyright Page; Contents; Preface; Chapter 1. Introduction; 1.1 Overview; 1.2 Problem and Scope; 1.3 Biological Data Integration; 1.4 Developing a Biological Data Integration System; References; Chapter 2. Challenges Faced in the Integration of Biological Information; 2.1 The Life Science Discovery Process; 2.2 An Information Integration Environment for Life Science Discovery; 2.3 The Nature of Biological Data; 2.4 Data Sources in Life Science; 2.5 Challenges in Information Integration; Conclusion; References 327 $aChapter 3. A Practitioner's Guide to Data Management and Data Integration in Bioinformatics3.1 Introduction; 3.2 Data Management in Bioinformatics; 3.3 Dimensions Describing the Space of Integration Solutions; 3.4 Use Cases of Integration Solutions; 3.5 Strengths and Weaknesses of the Various Approaches to Integration; 3.6 Tough Problems in Bioinformatics Integration; 3.7 Summary; Acknowledgments; References; Chapter 4. Issues to Address While Designing a Biological Information System; 4.1 Legacy; 4.2 A Domain in Constant Evolution; 4.3 Biological Queries; 4.4 Query Processing 327 $a4.5 Visualization4.6 Conclusion; Acknowledgments; References; Chapter 5. SRS: An Integration Platform for Databanks and Analysis Tools in Bioinformatics; 5.1 Integrating Flat File Databanks; 5.2 Integration of XML Databases; 5.3 Integrating Relational Databases; 5.4 The SRS Query Language; 5.5 Linking Databanks; 5.6 The Object Loader; 5.7 Scientific Analysis Tools; 5.8 Interfaces to SRS; 5.9 Automated Server Maintenance with SRS Prisma; 5.10 Conclusion; References; Chapter 6. The Kleisli Query System as a Backbone for Bioinformatics Data Integration and Analysis; 6.1 Motivating Example 327 $a6.2 Approach6.3 Data Model and Representation; 6.4 Query Capability; 6.5 Warehousing Capability; 6.6 Data Sources; 6.7 Optimizations; 6.8 User Interfaces; 6.9 Other Data Integration Technologies; 6.10 Conclusions; References; Chapter 7. Complex Query Formulation Over Diverse Information Sources in TAMBIS; 7.1 The Ontology; 7.2 The User Interface; 7.3 The Query Processor; 7.4 Related Work; 7.5 Current and Future Developments in TAMBIS; Acknowledgments; References; Chapter 8. The Information Integration System K2; 8.1 Approach; 8.2 Data Model and Languages; 8.3 An Example; 8.4 Internal Language 327 $a8.5 Data Sources8.6 Query Optimization; 8.7 User Interfaces; 8.8 Scalability; 8.9 Impact; 8.10 Summary; Acknowledgments; References; Chapter 9. P/FDM Mediator for a Bioinformatics Database Federation; 9.1 Approach; 9.2 Analysis; 9.3 Conclusions; Acknowledgment; References; Chapter 10. Integration Challenges in Gene Expression Data Management; 10.1 Gene Expression Data Management: Background; 10.2 The GeneExpress System; 10.3 Managing Gene Expression Data: Integration Challenges; 10.4 Integrating Third-Party Gene Expression Data in GeneExpress; 10.5 Summary; Acknowledgments; Trademarks 327 $aReferences 330 $aLife science data integration and interoperability is one of the most challenging problems facing bioinformatics today. In the current age of the life sciences, investigators have to interpret many types of information from a variety of sources: lab instruments, public databases, gene expression profiles, raw sequence traces, single nucleotide polymorphisms, chemical screening data, proteomic data, putative metabolic pathway models, and many others. Unfortunately, scientists are not currently able to easily identify and access this information because of the variety of semantics, interfaces, 410 0$aMorgan Kaufmann series in multimedia information and systems. 606 $aBioinformatics 615 0$aBioinformatics. 676 $a570/.285 701 $aLacroix$b Zoe?$01500555 701 $aCritchlow$b Terence$01500556 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910784532303321 996 $aBioinformatics$93727297 997 $aUNINA