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Data Mining Applications Using Ontologies in Biomedicine Unabridged edition [Hardback]

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  • Formāts: Hardback, 350 pages
  • Izdošanas datums: 31-Aug-2009
  • Izdevniecība: Artech House Publishers
  • ISBN-10: 1596933704
  • ISBN-13: 9781596933705
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Data Mining Applications Using Ontologies in Biomedicine Unabridged editionPalielināt
  • Formāts: Hardback, 350 pages
  • Izdošanas datums: 31-Aug-2009
  • Izdevniecība: Artech House Publishers
  • ISBN-10: 1596933704
  • ISBN-13: 9781596933705
Citas grāmatas par šo tēmu:
Permanent link: https://www.kriso.lv/db/9781596933705.html
Keywords:
Ontologies (sets of vocabulary terms whose meanings and relations with other terms are explicitly stated) are used in biomedical research for clustering and interpretation of biological data, protein classification, gene and pathway prediction, text mining, and connecting different databases. This book introduces emerging developments in bio-ontologies, focusing on algorithms and methodologies rather than on application domains. Examples at the molecular to clinical level represent areas of description logic, probability, and fuzzy logic. The book also covers data mining approaches, such as unsupervised learning, classification, and rule mining. Each self-contained chapter begins with a problem definition and historical perspective, then presents a mathematical or computational formulation of the problem, and describes computational methods and performance results. There are also details on technological infrastructure for bio-ontologies. About 160 illustrations support key topics. The book can be used as a text for upper undergraduate and beginning graduate courses in bioinformatics and medical informatics. It will also be useful to postdoctoral fellows, professional practitioners, and bioinformatics and medical informatics experts. Popescu is an adjunct professor in the Department of Computer Science, and Xu is professor and chair of the department, at the University of Missouri-Columbia. Annotation ©2009 Book News, Inc., Portland, OR (booknews.com)
Foreword xi
Preface xiii
Introduction to Ontologies
1(22)
Introduction
1(1)
History of Ontologies in Biomedicine
2(3)
The Philosophical Connection
2(1)
Recent Definition in Computer Science
2(1)
Origins of Bio-Ontologies
3(1)
Clinical and Medical Terminologies
4(1)
Recent Advances in Computer Science
4(1)
Form and Function of Ontologies
5(2)
Basic Components of Ontologies
5(1)
Components for Humans, Components for Computers
6(1)
Ontology Engineering
7(1)
Encoding Ontologies
7(3)
The OBO Format and the OBO Consortium
7(2)
OBO-Edit---The Open Biomedical Ontologies Editor
9(1)
OWL and RDF/XML
9(1)
Protege---An OWL Ontology Editor
10(1)
Spotlight on GO and UMLS
10(3)
The Gene Ontology
11(1)
The Unified Medical Language System
12(1)
Types and Examples of Ontologies
13(4)
Upper Ontologies
14(1)
Domain Ontologies
14(1)
Formal Ontologies
15(1)
Informal Ontologies
15(1)
Reference Ontologies
16(1)
Application Ontologies
16(1)
Bio-Ontologies
17(1)
Conclusion
17(6)
References
18(5)
Ontological Similarity Measures
23(22)
Introduction
23(7)
History
25(2)
Tversky's Parameterized Ratio Model of Similarity
27(1)
Aggregation in Similarity Assessment
28(2)
Traditional Approaches to Ontological Similarity
30(6)
Path-Based Measures
30(2)
Information Content Measures
32(3)
A Relationship Between Path-Based and Information-Content Measures
35(1)
New Approaches to Ontological Similarity
36(3)
Entity Class Similarity in Ontologies
36(1)
Cross-Ontological Similarity Measures
37(1)
Exploiting Common Disjunctive Ancestors
38(1)
Conclusion
39(6)
References
40(5)
Clustering with Ontologies
45(18)
Introduction
45(2)
Relational Fuzzy C-Means (NERFCM)
47(2)
Correlation Cluster Validity (CCV)
49(1)
Ontologicla SOM (OSOM)
50(2)
Examples of NERFCM, CCV, and OSOM Applications
52(7)
Test Dataset
52(1)
Clustering of the GPD194 Dataset Using NERFCM
53(1)
Determining the Number of Clusters of GPD194 Dataset Using CCV
54(2)
GPD194 Analysis Using OSOM
56(3)
Conclusion
59(4)
References
60(3)
Analyzing and Classifying Protein Family Data Using OWL Reasoning
63(20)
Introduction
63(3)
Analyzing Sequence Data
64(1)
The Protein Phosphatase Family
65(1)
Methods
66(4)
The Phosphatase Classification Pipeline
66(1)
The Datasets
66(1)
The Phosphatase Ontology
67(3)
Results
70(4)
Protein Phosphatases in Humans
70(1)
Results from the Analysis of A. Fumigatus
71(1)
Ontology System Versus A. Fumigatus Automated Annotation Pipeline
72(2)
Ontology Classification in the Comparative Analysis of Three Protozoan Parasites---A Case Study
74(4)
TriTryps Diseases
74(1)
TriTryps Protein Phosphatases
74(1)
Methods for the Protozoan Parasites
75(1)
Sequence Analysis Results from the TriTryps Phosphatome Study
75(2)
Evaluation of the Ontology Classification Method
77(1)
Conclusion
78(5)
References
79(4)
Go-Based Gene Function and Network Characterization
83(30)
Introduction
83(1)
GO-Based Functional Similarity
84(2)
GO Index-Based Functional Similarity
84(1)
Go Semantic Similarity
85(1)
Functional Relationship and High-Throughput Data
86(1)
Gene-Gene Relationship Revealed in Microarray Data
86(1)
The Relation Between Functional and Sequence Similarity
87(1)
Theoretical Basis for Building Relationship Among Genes Through Data
87(6)
Building the Relationship Among Genes Using One Dataset
87(2)
Meta-Analysis of Microarray Data
89(1)
Function Learning from Data
90(2)
Fuctional-Linkage Network
92(1)
Function-Prediction Algorithms
93(5)
Local Prediction
93(2)
Global Prediction Using a Boltzmann Machine
95(3)
Gene Function-Prediction Experiments
98(5)
Data Processing
98(1)
Sequence-Based Prediction
98(1)
Meta-Analysis of Yeast Microarray Data
99(2)
Case Study: Sin1 and PCBP2 Interactions
101(2)
Transcription Network Feature Analysis
103(4)
Time Delay in Transcriptional Regulation
104(1)
Kinetic Model for Time Series Microarray
104(1)
Regulatory Network Reconstruction
105(1)
GO-Enrichment Analysis
106(1)
Software Implementation
107(1)
Genefas
107(1)
Tools for Meta-Analysis
107(1)
Conclusion
107(6)
Acknowledgements
108(1)
References
108(5)
Mapping Genes to Biological Pathways Using Ontological Fuzzy Rule Systems
113(20)
Rule-Based Representation in Biomedical Applications
113(2)
Ontological Similarity as a Fuzzy Membership
115(2)
Ontological Fuzzy Rule System (OFRS)
117(3)
Application of OFRSs: Mapping Genes to Biological Pathways
120(11)
Mapping Gene to Pathways Using a Disjunctive OFRS
121(6)
Mapping Genes to Pathways Using an OFRS in an Evolutionary Framework
127(4)
Conclusion
131(2)
Acknowledgments
131(1)
References
131(2)
Extracting Biological Knowledge by Association Rule Mining
133(30)
Association Rule Mining and Fuzzy Association Rule Mining Overview
133(11)
Association Rules: Formal Definition
134(3)
Association Rule Mining Algorithms
137(1)
Apriori Algorithm
138(2)
Fuzzy Association Rules
140(4)
Using GO in Association Rule Mining
144(8)
Unveiling Biological Associations by Extracting Rules Involving GO Terms
144(3)
Giving Biological Significance to Rule Sets by Using GO
147(3)
Other Joint Applications of Associations Rules and GO
150(2)
Applications for Extracting Knowledge from Microarray Data
152(11)
Association Rules That Relate Gene Expression Patterns with Other Features
153(2)
Association Rules to Obtain Relations Between Genes and Their Expression Values
155(2)
Acknowledgements
157(1)
References
157(6)
Text Summarization Using Ontologies
163(22)
Introduction
163(1)
Representing Background Knowledge---Ontology
164(3)
An Algebraic Approach to Ontologies
165(1)
Modeling Ontologies
166(1)
Deriving Similarity
167(1)
Referencing the Background Knowledge---Providing Descriptions
167(6)
Instantiated Ontology
170(3)
Data Summarization Through Background Knowledge
173(8)
Connectivity Clustering
173(4)
Similarity Clustering
177(4)
Conclusion
181(4)
References
182(3)
Reasoning over Anatomical Ontologies
185(34)
Why Reasoning Matters
185(2)
Data, Reasoning, and a New Frontier
187(8)
A Taxonomy of Data and Reasoning
187(2)
Contemporary Reasoners
189(4)
Anatomy as a New Frontier for Biological Reasoners
193(2)
Biological Ontologies Today
195(10)
Current Practices
195(1)
Structural Issues That Limit Reasoning
196(1)
A Biological Example: The Maize Tassel
197(2)
Representational Issues
199(6)
Facilitating Reasoning About Anatomy
205(3)
Link Different Kinds of Knowledge
206(1)
Layer on Top of the Ontology
206(1)
Change the Representation
207(1)
Some Visions for the Future
208(11)
Acknowledgments
208(1)
References
209(10)
Ontology Applications in Text Mining
219(30)
Introduction
219(1)
What Is Text Mining?
219(1)
Ontologies
220(1)
The Importance of Ontology to Text Mining
220(2)
Semantic Document Clustering and Summarization: Ontology Applications in Text Mining
222(13)
Introduction to Document Clustering
222(1)
The Graphical Representation Model
223(5)
Graph Clustering for Graphical Representations
228(2)
Text Summarization
230(3)
Document Clustering and Summarization with Graphical Representation
233(2)
Swanson's Undiscovered Public Knowledge (UDPK)
235(11)
How does UDPK Work?
236(1)
A Semantic Version of Swanson's UDPK Model
237(1)
The Bio-SbKDS Algorithm
238(8)
Conclusion
246(3)
References
247(2)
About the Editors 249(1)
List of Contributors 250(3)
Index 253
Dong Xu is a professor and chair of the Department of Computer Science at the University of Missouri, Columbia. He holds an M.S. in solid state physics from Peking University and a Ph.D. in computational biology from the University of Illinois. Mihail Popuscu is a adjunct professor in the Department of Computer Science and an assistant professor in the Department of Health Management and Informatics at the University of Missouri, Columbia. He holds an M.S. in electrical and computer engineering, an M.S. medical physics, and a Ph.D. in computer engineering from the University of Missouri, Columbia.