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.
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.
|Series:||Chapman & Hall/CRC Mathematical and Computational Biology|
|Sold by:||Barnes & Noble|
|File size:||3 MB|
About the Author
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.
Table of Contents
Ontologies and Applications of Ontologies in Biomedicine
What Is an Ontology?
Ontologies and Bio-Ontologies
Ontologies for Data Organization, Integration, and Searching
Computer Reasoning with Ontologies
Typical Applications of Bio-Ontologies
Mathematical Logic and Inference
Representation and Logic
Probability Theory and Statistics for Bio-Ontologies
Introduction to Graphs
RDF and RDFS
OWL and the Semantic Web
The Gene Ontology
A Tool for the Unification of Biology
Relations in GO
Basic Formal Ontology
The Big Divide: Continuants and Occurrents
Universals and Particulars
Revisiting Gene Ontology
Revisiting GO Annotations
A Selective Survey of Bio-Ontologies
The National Center for Biomedical Ontology
What Makes a Good Ontology?
GRAPH ALGORITHMS FOR BIO-ONTOLOGIES
Multiple Testing Problem
Term-for-Term Analysis: An Extended Example
Inferred Annotations Lead to Statistical Dependencies in Ontology DAGs
Parent-Child Analysis: An Extended Example
Topology-elim: An Extended Example
Model-Based Approaches to GO Analysis
A Probabilistic Generative Model for GO Enrichment Analysis
A Bayesian Network Model
MGSA: An Extended Example
Information Content in Ontologies
Semantic Similarity of Genes and Other Items Annotated by Ontology Terms
Statistical Significance of Semantic Similarity Scores
Frequency-Aware Bayesian Network Searches in Attribute Ontologies
Probabilistic Inference for the Items
The Frequency-Aware Network
INFERENCE IN ONTOLOGIES
Inference in the Gene Ontology
Inference over GO Edges
Cross-Products and Logical Definitions
RDFS Semantics and Inference
Inference in OWL Ontologies
The Semantics of Equality
The Semantics of Properties
The Semantics of Classes
The Semantics of the Schema Vocabulary
Algorithmic Foundations of Computational Inference
The Tableau Algorithm
Combining RDF Graphs
Appendix A: An Overview of R
Appendix B: Information Content and Entropy
Appendix C: W3C Standards: XML, URIs, and RDF
Appendix D: W3C Standards: OWL
Exercises and Further Reading appear at the end of each chapter.