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E-grāmata: Introduction to Bio-Ontologies

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(Charite Universitatsmedizin Berlin, Germany), (Charite Universitatsmedizin Berlin, Germany)
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Introduction to Bio-Ontologies explores the computational background of ontologies. Emphasizing computational and algorithmic issues surrounding bio-ontologies, this self-contained text helps readers understand ontological algorithms and their applications.

The first part of the book defines ontology and bio-ontologies. It also explains the importance of mathematical logic for understanding concepts of inference in bio-ontologies, discusses the probability and statistics topics necessary for understanding ontology algorithms, and describes ontology languages, including OBO (the preeminent language for bio-ontologies), RDF, RDFS, and OWL.

The second part covers significant bio-ontologies and their applications. The book presents the Gene Ontology; upper-level ontologies, such as the Basic Formal Ontology and the Relation Ontology; and current bio-ontologies, including several anatomy ontologies, Chemical Entities of Biological Interest, Sequence Ontology, Mammalian Phenotype Ontology, and Human Phenotype Ontology.

The third part of the text introduces the major graph-based algorithms for bio-ontologies. The authors discuss how these algorithms are used in overrepresentation analysis, model-based procedures, semantic similarity analysis, and Bayesian networks for molecular biology and biomedical applications.

With a focus on computational reasoning topics, the final part describes the ontology languages of the Semantic Web and their applications for inference. It covers the formal semantics of RDF and RDFS, OWL inference rules, a key inference algorithm, the SPARQL query language, and the state of the art for querying OWL ontologies.

Web Resource
Software and data designed to complement material in the text are available on the book’s website: http://bio-ontologies-book.org The site provides the R Robo package developed for the book, along with a compressed archive of data and ontology files used in some of the exercises. It also offers teaching/presentation slides and links to other relevant websites.

This book provides readers with the foundation to use ontologies as a starting point for new bioinformatics research projects or to support current molecular genetics research projects. By supplying a self-contained introduction to OBO ontologies and the Semantic Web, it bridges the gap between both fields and helps readers see what each can contribute to the analysis and understanding of biomedical data.

Recenzijas

"This book is one of the first source books in the field; it is well written and coherent. Its introduction gives the reader a good taste of what comes next and it also contains good exercises." Mohsen Mahmoudi Aznaveh, ACM SIGACT News, 2013

"This welcome book could have been titled all you wanted to know about bio-ontologies but didnt dare ask. In recent years the biological sciences have generated very large, complex data sets whose management, analysis and sharing have created unprecedented challenges. The development of ontologies, originally driven by the invention of the semantic web, has been critical in handling this data and permitting interoperability between databases and between applications. Many of the bio-ontologies and the computational approaches which use them have now become mature, and an understanding of bio-ontologies has really become a requirement for anyone in the mainstream biomedical sciences. Introduction to Bio-Ontologies provides a self-contained introduction to ontologies for bioinformaticians, computer scientists and biomedical scientists who need to know about the computational background and implementation of ontologies. The book is designed to support either advanced undergraduate or masters courses in bioinformatics or computer science but is also a first stop for any investigator who wants to understand ontologies and how to use them. The four parts of the book cover basic concepts, specific widely used ontologies, such as the Gene Ontology, algorithms and applications of ontologies. The breadth of coverage is impressive for such a compact volume and there is excellent critical discussion of ontologies from a biological as well as a computational point of view. The book succeeds well in its aim of providing a self-contained primer on ontologies and much of the mathematics used is backed up with detailed explanations and technical appendices which introduce and explain the more co

List of Figures
xxi
List of Tables
xxv
Symbol Description xxvii
I Basic Concepts
1(112)
1 Ontologies and Applications of Ontologies in Biomedicine
3(8)
1.1 What Is an Ontology?
3(2)
1.2 Ontologies and Bio-Ontologies
5(1)
1.3 Ontologies for Data Organization, Integration, and Searching
6(3)
1.4 Computer Reasoning with Ontologies
9(1)
1.5 Typical Applications of Bio-Ontologies
10(1)
2 Mathematical Logic and Inference
11(30)
2.1 Representation and Logic
11(2)
2.2 Propositional Logic
13(5)
2.3 First-Order Logic
18(7)
2.4 Sets
25(4)
2.5 Description Logic
29(7)
2.5.1 Description Language ALC
29(2)
2.5.2 Description Language ALC
31(1)
2.5.3 Further Description Logic Constructors
32(4)
2.6 Exercises and Further Reading
36(5)
3 Probability Theory and Statistics for Bio-Ontologies
41(26)
3.1 Probability Theory
41(15)
3.1.1 Hypothesis Testing
43(1)
3.1.2 p-Values and Probability Distributions
44(7)
3.1.3 Multiple-Testing Correction
51(5)
3.2 Bayes' Theorem
56(2)
3.3 Introduction to Graphs
58(4)
3.4 Bayesian Networks
62(2)
3.5 Exercises and Further Reading
64(3)
4 Ontology Languages
67(46)
4.1 OBO
67(4)
4.1.1 OBO Stanzas
68(2)
4.1.2 Intersections: Computable Definitions
70(1)
4.2 OWL and the Semantic Web
71(28)
4.2.1 Resource Description Framework
72(6)
4.2.2 RDF Schema
78(7)
4.2.3 The Web Ontology Language OWL
85(10)
4.2.4 OBO, RDF, RDFS, and OWL
95(4)
4.3 Exercises and Further Reading
99(14)
II Bio-Ontologies
113(66)
5 The Gene Ontology
115(24)
5.1 A Tool for the Unification of Biology
115(2)
5.2 Three Subontologies
117(3)
5.2.1 Molecular Function
117(1)
5.2.2 Biological Process
117(1)
5.2.3 Cellular Component
118(2)
5.3 Relations in GO
120(1)
5.4 GO Annotations
121(12)
5.4.1 Evidence for Gene Functions
124(3)
5.4.2 Inferred from Electronic Annotation
127(1)
5.4.3 The True Path Rule and Propagation of Annotations
128(5)
5.5 GO Slims
133(1)
5.6 Exercises and Further Reading
134(5)
6 Upper-Level Ontologies
139(14)
6.1 Basic Formal Ontology
139(1)
6.2 The Big Divide: Continuants and Occurrents
140(2)
6.2.1 Continuants
141(1)
6.2.2 Occurrents
142(1)
6.3 Universals and Particulars
142(1)
6.4 Relation Ontology
143(4)
6.5 Revisiting Gene Ontology
147(1)
6.6 Revisiting GO Annotations
148(2)
6.7 Exercises and Further Reading
150(3)
7 A Selective Survey of Bio-Ontologies
153(26)
7.1 OBO Foundry
153(2)
7.2 The National Center for Biomedical Ontology
155(1)
7.3 Bio-Ontologies
155(20)
7.3.1 Ontologies for Anatomy: The FMA and Model Organisms
156(3)
7.3.2 Cell Ontology
159(1)
7.3.3 Chemical Entities of Biological Interest
160(2)
7.3.4 OBI
162(1)
7.3.5 The Protein Ontology
162(3)
7.3.6 The Sequence Ontology
165(1)
7.3.7 Mammalian Phenotype Ontology (MPO)
166(1)
7.3.8 Human Phenotype Ontology (HPO)
167(4)
7.3.9 MPATH
171(1)
7.3.10 PATO
172(3)
7.4 What Makes a Good Ontology?
175(2)
7.5 Exercises and Further Reading
177(2)
III Graph Algorithms for Bio-Ontologies
179(100)
8 Overrepresentation Analysis
181(38)
8.1 Definitions
182(1)
8.2 Term-for-Term
183(2)
8.3 Multiple Testing Problem
185(3)
8.4 Term-for-Term Analysis: An Extended Example
188(4)
8.5 Inferred Annotations Lead to Statistical Dependencies in Ontology DAGs
192(3)
8.6 Parent-Child Algorithms
195(3)
8.7 Parent-Child Analysis: An Extended Example
198(2)
8.8 Topology-Based Algorithms
200(5)
8.8.1 Elim
200(2)
8.8.2 Weight
202(3)
8.9 Topology-elim: An Extended Example
205(2)
8.10 Other Approaches
207(2)
8.11 Summary
209(1)
8.12 Exercises and Further Reading
209(10)
9 Model-Based Approaches to GO Analysis
219(18)
9.1 A Probabilistic Generative Model for GO Enrichment Analysis
219(3)
9.2 A Bayesian Network Model
222(11)
9.2.1 Maximum a posteriori
226(1)
9.2.2 Monte Carlo Markov Chain Algorithm
227(3)
9.2.3 MGSA Algorithm with Unknown Parameters
230(3)
9.3 MGSA: An Extended Example
233(1)
9.4 Summary
234(1)
9.5 Exercises and Further Reading
235(2)
10 Semantic Similarity
237(24)
10.1 Information Content in Ontologies
237(10)
10.2 Semantic Similarity of Genes and Other Items Annotated by Ontology Terms
247(5)
10.2.1 Graph-Based and Set-Based Measures of Semantic Similarity
248(1)
10.2.2 Applications of Semantic Similarity in Bioinformatics
249(2)
10.2.3 Applications of Semantic Similarity for Clinical Diagnostics
251(1)
10.3 Statistical Significance of Semantic Similarity Scores
252(3)
10.4 Exercises and Further Reading
255(6)
11 Frequency-Aware Bayesian Network Searches in Attribute Ontologies
261(18)
11.1 Modeling Queries
262(8)
11.1.1 High-Level Description of the Model
262(2)
11.1.2 Annotation Propagation Rule for Bayesian Networks
264(2)
11.1.3 LPDs of Hidden Term States
266(1)
11.1.4 LPDs of Observed Term States
266(4)
11.2 Probabilistic Inference for the Items
270(2)
11.3 Parameter-Augmented Network
272(1)
11.4 The Frequency-Aware Network
273(1)
11.5 Benchmark
274(5)
IV Inference in Ontologies
279(96)
12 Inference in the Gene Ontology
281(12)
12.1 Inference over GO Edges
281(4)
12.2 Cross-Products and Logical Definitions
285(4)
12.2.1 Intra-GO Cross-Product Definitions
286(2)
12.2.2 External Cross-Product Definitions
288(1)
12.2.3 Reasoning with Cross-Product Definitions
288(1)
12.3 Exercises and Further Reading
289(4)
13 RDFS Semantics and Inference
293(24)
13.1 Definitions
293(1)
13.2 Interpretations
294(9)
13.3 RDF Entailment
303(2)
13.4 RDFS Entailment
305(2)
13.5 Entailment Rules
307(7)
13.6 Summary
314(1)
13.7 Exercises and Further Reading
314(3)
14 Inference in OWL Ontologies
317(18)
14.1 The Semantics of Equality
317(3)
14.2 The Semantics of Properties
320(5)
14.3 The Semantics of Classes
325(6)
14.4 The Semantics of the Schema Vocabulary
331(2)
14.5 Conclusions
333(1)
14.6 Exercises and Further Reading
333(2)
15 Algorithmic Foundations of Computational Inference
335(22)
15.1 The Tableau Algorithm
336(10)
15.1.1 Negative Normal Form
338(1)
15.1.2 Algorithm for ABox
339(5)
15.1.3 Adding Support for the TBox
344(2)
15.2 Developer Libraries
346(2)
15.3 Exercises and Further Reading
348(9)
16 SPARQL
357(18)
16.1 SPARQL Queries
357(8)
16.2 Combining RDF Graphs
365(3)
16.3 Conclusions
368(1)
16.4 Exercises and Further Reading
369(6)
Appendices
375(82)
A An Overview of R
377(18)
B Information Content and Entropy
395(4)
C W3C Standards: XML, URIs, and RDF
399(28)
D W3C Standards: OWL
427(30)
Glossary 457(4)
Bibliography 461(24)
Index 485
Peter N. Robinson is a research scientist and leader of the Computational Biology Group in the Institute of Medical Genetics and Human Genetics at Charité-Universitätsmedizin Berlin. Dr. Robinson completed his medical education at the University of Pennsylvania, followed by an internship at Yale University. He also studied mathematics and computer science at Columbia University. His research interests involve the use of mathematical and bioinformatics models to understand biology and hereditary disease.

Sebastian Bauer is a research assistant in the Institute of Medical Genetics and Human Genetics at Charité-Universitätsmedizin Berlin. He earned a degree in computer science from the Technical University of Ilmenau. His research interests include mathematical modeling, discrete algorithms, theoretical computer science, software engineering, and the applications of these fields to medicine and biology.
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