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Ontologies for Bioinformatics [Kõva köide]

(College of Computer Science), (Tulane University)
  • Formaat: Hardback, 440 pages, kõrgus x laius x paksus: 229x178x25 mm, kaal: 953 g, 70 illus.
  • Sari: Computational Molecular Biology
  • Ilmumisaeg: 23-Sep-2005
  • Kirjastus: MIT Press
  • ISBN-10: 0262025914
  • ISBN-13: 9780262025911
Teised raamatud teemal:
  • Formaat: Hardback, 440 pages, kõrgus x laius x paksus: 229x178x25 mm, kaal: 953 g, 70 illus.
  • Sari: Computational Molecular Biology
  • Ilmumisaeg: 23-Sep-2005
  • Kirjastus: MIT Press
  • ISBN-10: 0262025914
  • ISBN-13: 9780262025911
Teised raamatud teemal:
Ontologies as a critical framework for the vast amounts of data in the postgenomic era: an introduction to the basic concepts and applications of ontologies and ontology languages for the life sciences.

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.
Preface xi
I Introduction to Ontologies
1(126)
Hierarchies and Relationships
3(32)
Traditional Record Structures
3(2)
The extensible Markup Language
5(2)
Hierarchical Organization
7(3)
Creating and Updating XML
10(7)
The Meaning of a Hierarchy
17(8)
Relationships
25(3)
Namespaces
28(4)
Exercises
32(3)
XML Semantics
35(16)
The Meaning of Meaning
35(3)
Infosets
38(4)
XML Schema
42(4)
XML Data
46(3)
Exercises
49(2)
Rules and Inference
51(10)
Introduction to Rule-Based Systems
51(3)
Forward-and Backward-Chaining Rule Engines
54(2)
Theorem Provers and Other Reasoners
56(3)
Performance of Automated Reasoners
59(2)
The Semantic Web and Bioinformatics Applications
61(28)
The Semantic Web in Bioinformatics
61(2)
The Resource Description Framework
63(14)
XML Topic Maps
77(2)
The Web Ontology Language
79(8)
Exercises
87(2)
Survey of Ontologies in Bioinformatics
89(38)
Bio-Ontologies
89(10)
Unified Medical Language System
90(2)
The Gene Ontology
92(6)
Ontologies of Bioinformatics Ontologies
98(1)
Ontology Languages in Bioinformatics
99(7)
Macromolecular Sequence Databases
106(2)
Nucleotide Sequence Databases
107(1)
Protein Sequence Databases
108(1)
Structural Databases
108(7)
Nucleotide Structure Databases
108(1)
Protein Structure Databases
109(6)
Transcription Factor Databases
115(1)
Species-Specific Databases
116(2)
Specialized Protein Databases
118(1)
Gene Expression Databases
119(2)
Transcriptomics Databases
119(1)
Proteomics Databases
120(1)
Pathway Databases
121(2)
Single Nucleotide Polymorphisms
123(4)
II Building and Using Ontologies
127(192)
Information Retrieval
129(26)
The Search Process
129(2)
Vector Space Retrieval
131(9)
Using Ontologies for Formulating Queries
140(2)
Organizing by Citation
142(4)
Vector Space Retrieval of Knowledge Representations
146(2)
Retrieval of Knowledge Representations
148(7)
Sequence Similarity Searching Tools
155(20)
Basic Concepts
155(3)
Dynamic Programming Algorithm
158(1)
FASTA
159(2)
BLAST
161(13)
The BLAST Algorithm
161(3)
BLAST Search Types
164(2)
Scores and Values
166(2)
BLAST Variants
168(6)
Exercises
174(1)
Query Languages
175(12)
XML Navigation Using XPath
176(4)
Querying XML Using XQuery
180(3)
Semantic Web Queries
183(1)
Exercises
184(3)
The Transformation Process
187(16)
Experimental and Statistical Methods as Transformations
187(3)
Presentation of Information
190(5)
Changing the Point of View
195(2)
Transformation Techniques
197(3)
Automating Transformations
200(3)
Transforming with Traditional Programming Languages
203(58)
Text Transformations
204(30)
Line-Oriented Transformation
205(12)
Multidimensional Arrays
217(5)
Perl Procedures
222(3)
Pattern Matching
225(5)
Perl Data Structures
230(4)
Transforming XML
234(25)
Using Perl Modules and Objects
234(2)
Processing XML Elements
236(8)
The Document Object Model
244(1)
Producing XML
245(8)
Transforming XML to XML
253(6)
Exercises
259(2)
The XML Transformation Language
261(20)
Transformation as Digestion
261(4)
Programming in XSLT
265(2)
Navigation and Computation
267(2)
Conditionals
269(2)
Precise Formatting
271(2)
Multiple Source Documents
273(2)
Procedural Programming
275(5)
Exercises
280(1)
Building Bioinformatics Ontologies
281(38)
Purpose of Ontology Development
282(3)
Selecting an Ontology Language
285(3)
Ontology Development Tools
288(3)
Acquiring Domain Knowledge
291(2)
Reusing Existing Ontologies
293(3)
Designing the Concept Hierarchy
296(7)
Uniform Hierarchy
300(1)
Classes vs. Instances
301(1)
Ontological Commitment
301(1)
Strict Taxonomies
302(1)
Designing the Properties
303(10)
Classes vs. Property Values
305(2)
Domain and Range Constraints
307(3)
Cardinality Constraints
310(3)
Validating and Modifying the Ontology
313(5)
Exercises
318(1)
III Reasoning with Uncertainty
319(74)
Inductive vs. Deductive Reasoning
321(10)
Sources and Semantics of Uncertainty
322(2)
Extensional Approaches to Uncertainty
324(1)
Intensional Approaches to Uncertainty
325(6)
Bayesian Networks
331(24)
The Bayesian Network Formalism
332(3)
Stochastic Inference
335(6)
Constructing Bayesian Networks
341(13)
BN Requirements
342(1)
Machine Learning
343(3)
Building BNs from Components
346(1)
Ontologies as BNs
347(1)
BN Design Patterns
348(3)
Validating and Revising BNs
351(3)
Exercises
354(1)
Combining Information
355(14)
Combining Discrete Information
356(3)
Combining Continuous Information
359(2)
Information Combination as a BN Design Pattern
361(2)
Measuring Probability
363(2)
Dempster-Shafer Theory
365(4)
The Bayesian Web
369(10)
Introduction
369(1)
Requirements for Bayesian Network Interoperability
370(1)
Extending the Semantic Web
371(1)
Ontologies for Bayesian Networks
372(7)
Answers to Selected Exercises
379(14)
References 393(20)
Index 413