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Ontologies for Bioinformatics : Computational Molecular Biology - Kenneth Baclawski

Ontologies for Bioinformatics

Computational Molecular Biology

Hardcover

Published: 1st October 2005
For Ages: 18+ years old
Ships: 3 to 4 business days
3 to 4 business days
RRP $31.99
$29.75

Recent advances in biotechnology, spurred by the Human Genome Project, have resulted in the accumulation of vast amounts of new data. Ontologies -- computer-readable, precise formulations of concepts (and the relationship among them) in a given field -- are a critical framework for coping with the exponential growth of valuable biological data generated by high-output technologies. This book introduces the key concepts and applications of ontologies and ontology languages in bioinformatics and will be an essential guide for bioinformaticists, computer scientists, and life science researchers.The three parts of Ontologies for Bioinformatics ask, and answer, three pivotal questions: what ontologies are; how ontologies are used; and what ontologies could be (which focuses on how ontologies could be used for reasoning with uncertainty). The authors first introduce the notion of an ontology, from hierarchically organized ontologies to more general network organizations, and survey the best-known ontologies in biology and medicine. They show how to construct and use ontologies, classifying uses into three categories: querying, viewing, and transforming data to serve diverse purposes. Contrasting deductive, or Boolean, logic with inductive reasoning, they describe the goal of a synthesis that supports both styles of reasoning. They discuss Bayesian networks as a way of expressing uncertainty, describe data fusion, and propose that the World Wide Web can be extended to support reasoning with uncertainty. They call this inductive reasoning web the Bayesian web.

"Given the current explosion of biological data in multiple dimensions, it is time to think systematically about strategies and techniques to not only store, but also integrate and represent them in knowledge-oriented ways. Ontology is the solution, and this book is an excellent effort to evaluate a number of alternative ontology-exchange languages, and to recommend them for use within the larger bioinformatics community."--Bo Yuan, Departments of Biomedical Informatics and Pharmacology, The Ohio State University

Prefacep. xi
Introduction to Ontologiesp. 1
Hierarchies and Relationshipsp. 3
Traditional Record Structuresp. 3
The eXtensible Markup Languagep. 5
Hierarchical Organizationp. 7
Creating and Updating XMLp. 10
The Meaning of a Hierarchyp. 17
Relationshipsp. 25
Namespacesp. 28
Exercisesp. 32
XML Semanticsp. 35
The Meaning of Meaningp. 35
Infosetsp. 38
XML Schemap. 42
XML Datap. 46
Exercisesp. 49
Rules and Inferencep. 51
Introduction to Rule-Based Systemsp. 51
Forward- and Backward-Chaining Rule Enginesp. 54
Theorem Provers and Other Reasonersp. 56
Performance of Automated Reasonersp. 59
The Semantic Web and Bioinformatics Applicationsp. 61
The Semantic Web in Bioinformaticsp. 61
The Resource Description Frameworkp. 63
XML Topic Mapsp. 77
The Web Ontology Languagep. 79
Exercisesp. 87
Survey of Ontologies in Bioinformaticsp. 89
Bio-Ontologiesp. 89
Unified Medical Language Systemp. 90
The Gene Ontologyp. 92
Ontologies of Bioinformatics Ontologiesp. 98
Ontology Languages in Bioinformaticsp. 99
Macromolecular Sequence Databasesp. 106
Nucleotide Sequence Databasesp. 107
Protein Sequence Databasesp. 108
Structural Databasesp. 108
Nucleotide Structure Databasesp. 108
Protein Structure Databasesp. 109
Transcription Factor Databasesp. 115
Species-Specific Databasesp. 116
Specialized Protein Databasesp. 118
Gene Expression Databasesp. 119
Transcriptomics Databasesp. 119
Proteomics Databasesp. 120
Pathway Databasesp. 121
Single Nucleotide Polymorphismsp. 123
Building and Using Ontologiesp. 127
Information Retrievalp. 129
The Search Processp. 129
Vector Space Retrievalp. 131
Using Ontologies for Formulating Queriesp. 140
Organizing by Citationp. 142
Vector Space Retrieval of Knowledge Representationsp. 146
Retrieval of Knowledge Representationsp. 148
Sequence Similarity Searching Toolsp. 155
Basic Conceptsp. 155
Dynamic Programming Algorithmp. 158
Fastap. 159
Blastp. 161
The BLAST Algorithmp. 161
BLAST Search Typesp. 164
Scores and Valuesp. 166
Blast Variantsp. 168
Exercisesp. 174
Query Languagesp. 175
XML Navigation Using XPathp. 176
Querying XML Using XQueryp. 180
Semantic Web Queriesp. 183
Exercisesp. 184
The Transformation Processp. 187
Experimental and Statistical Methods as Transformationsp. 187
Presentation of Informationp. 190
Changing the Point of Viewp. 195
Transformation Techniquesp. 197
Automating Transformationsp. 200
Transforming with Traditional Programming Languagesp. 203
Text Transformationsp. 204
Line-Oriented Transformationp. 205
Multidimensional Arraysp. 217
Perl Proceduresp. 222
Pattern Matchingp. 225
Perl Data Structuresp. 230
Transforming XMLp. 234
Using Perl Modules and Objectsp. 234
Processing XML Elementsp. 236
The Document Object Modelp. 244
Producing XMLp. 245
Transforming XML to XMLp. 253
Exercisesp. 259
The XML Transformation Languagep. 261
Transformation as Digestionp. 261
Programming in XSLTp. 265
Navigation and Computationp. 267
Conditionalsp. 269
Precise Formattingp. 271
Multiple Source Documentsp. 273
Procedural Programmingp. 275
Exercisesp. 280
Building Bioinformatics Ontologiesp. 281
Purpose of Ontology Developmentp. 282
Selecting an Ontology Languagep. 285
Ontology Development Toolsp. 288
Acquiring Domain Knowledgep. 291
Reusing Existing Ontologiesp. 293
Designing the Concept Hierarchyp. 296
Uniform Hierarchyp. 300
Classes vs. Instancesp. 301
Ontological Commitmentp. 301
Strict Taxonomiesp. 302
Designing the Propertiesp. 303
Classes vs. Property Valuesp. 305
Domain and Range Constraintsp. 307
Cardinality Constraintsp. 310
Validating and Modifying the Ontologyp. 313
Exercisesp. 318
Reasoning with Uncertaintyp. 319
Inductive vs. Deductive Reasoningp. 321
Sources and Semantics of Uncertaintyp. 322
Extensional Approaches to Uncertaintyp. 324
Intensional Approaches to Uncertaintyp. 325
Bayesian Networksp. 331
The Bayesian Network Formalismp. 332
Stochastic Inferencep. 335
Constructing Bayesian Networksp. 341
BN Requirementsp. 342
Machine Learningp. 343
Building BNs from Componentsp. 346
Ontologies as BNsp. 347
BN Design Patternsp. 348
Validating and Revising BNsp. 351
Exercisesp. 354
Combining Informationp. 355
Combining Discrete Informationp. 356
Combining Continuous Informationp. 359
Information Combination as a BN Design Patternp. 361
Measuring Probabilityp. 363
Dempster-Shafer Theoryp. 365
The Bayesian Webp. 369
Introductionp. 369
Requirements for Bayesian Network Interoperabilityp. 370
Extending the Semantic Webp. 371
Ontologies for Bayesian Networksp. 372
Answers to Selected Exercisesp. 379
Referencesp. 393
Indexp. 413
Table of Contents provided by Ingram. All Rights Reserved.

ISBN: 9780262025911
ISBN-10: 0262025914
Series: Computational Molecular Biology
Audience: Professional
For Ages: 18+ years old
Format: Hardcover
Language: English
Number Of Pages: 440
Published: 1st October 2005
Publisher: MIT Press Ltd
Country of Publication: US
Dimensions (cm): 22.9 x 17.8  x 3.2
Weight (kg): 0.95