Data Mining in Biomedicine Using Ontologies

Data Mining in Biomedicine Using Ontologies

by Mihail Popescu
     
 

ISBN-10: 1596933704

ISBN-13: 9781596933705

Pub. Date: 08/28/2009

Publisher: Artech House, Incorporated

Presently, a growing number of ontologies are being built and used for annotating data in biomedical research. Thanks to the tremendous amount of data being generated, ontologies are now being used in numerous ways, including connecting different databases, refining search capabilities, interpreting experimental/clinical data, and inferring knowledge. This

Overview

Presently, a growing number of ontologies are being built and used for annotating data in biomedical research. Thanks to the tremendous amount of data being generated, ontologies are now being used in numerous ways, including connecting different databases, refining search capabilities, interpreting experimental/clinical data, and inferring knowledge. This cutting-edge resource introduces you to latest developments in bio-ontologies. The book provides you with the theoretical foundations and examples of ontologies, as well as applications of ontologies in biomedicine, from molecular levels to clinical levels. You also find details on technological infrastructure for bio-ontologies. This comprehensive, one-stop volume presents a wide range of practical bio-ontology information, offering you detailed guidance in the clustering of biological data, protein classification, gene and pathway prediction, and text mining. More than 160 illustrations support key topics throughout the book.

Product Details

ISBN-13:
9781596933705
Publisher:
Artech House, Incorporated
Publication date:
08/28/2009
Pages:
262
Product dimensions:
7.20(w) x 10.20(h) x 0.80(d)

Table of Contents

Introduction in Ontologies. UMLS. Gene Ontology. Ontological Similarity Measures. Clustering Objects Described by Ontology Terms. Protein Classification Using Ontology. GO-based Gene Function Prediction Using High-Throughput Data. Mapping Genes to Gene Networks Using Ontological Fuzzy Rule Systems. Extracting Biological Knowledge by Fuzzy Association Rule Mining. Data Summarization Using Ontologies. Data Mining through Ontology Learning. Integrating Databases for Data Mining Using UMLS. Ontology Application in Text Mining.

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