Introduction to Bio-Ontologies

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A01=Peter N. Robinson
A01=Sebastian Bauer
advanced ontology inference techniques
Annotation Propagation Rule
Author_Peter N. Robinson
Author_Sebastian Bauer
Basic Formal Ontology
bayesian
Bayesian Network
Bayesian networks
Bio-Ontologies
bioinformatics
Biological Process Term
biomedical data
biomedical data analysis
Biomedical Ontologies
blank
Blank Nodes
Category=PS
Category=UNF
Category=UY
Chemical Entities of Biological Interest
computational biology
Description Logics
entailment
Entailment Rules
eq_bestseller
eq_computing
eq_isMigrated=1
eq_isMigrated=2
eq_nobargain
eq_non-fiction
eq_science
formal semantics
gene
Gene Ontology
Genes Annotated
GO
graph algorithms
graph-based algorithms
Human Phenotype Ontology
inference algorithm
Inference Rule
Mammalian Phenotype Ontology
mathematical logic
molecular biology
Molecular Function
molecular genetics
molecular genetics research
network
node
Obo
ontological algorithms
ontology
Ontology Languages
ontology reasoning
Overrepresentation Analysis
OWL
Owl Ontology
Phenotypic Abnormality
RDF
RDF Data
RDFS
rules
semantic
Semantic Similarity
Semantic Similarity Analysis
Semantic Web
SPARQL
SPARQL Query
Study Set
Ubiquitin Ligase Complex
URI Reference
W3C standards
web

Product details

  • ISBN 9780367659271
  • Weight: 453g
  • Dimensions: 156 x 234mm
  • Publication Date: 30 Sep 2020
  • Publisher: Taylor & Francis Ltd
  • Publication City/Country: GB
  • Product Form: Paperback
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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 ResourceSoftware 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.

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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