Data mining in biomedicine using ontologies /:
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...
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| Other Authors: | , |
|---|---|
| Format: | Electronic eBook |
| Language: | English |
| Published: |
Norwood, MA :
Artech House,
©2009.
|
| Series: | Artech House bioinformatics & biomedical imaging series.
|
| Subjects: | |
| Online Access: | DE-862 DE-863 |
| Summary: | 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. |
| Physical Description: | 1 online resource (xv, 262 pages) : illustrations. |
| Bibliography: | Includes bibliographical references and index. |
| ISBN: | 9781596933712 1596933712 |
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| 245 | 0 | 0 | |a Data mining in biomedicine using ontologies / |c Mihail Popescu, Dong Xu, editors. |
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| 520 | 3 | |a 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. |c Publisher abstract. | |
| 505 | 0 | |a Data Mining in Biomedicine Using Ontologies; Contents; Foreword; Preface; Chapter 1 Introduction to Ontologies; 1.1 Introduction; 1.2 History of Ontologies in Biomedicine; 1.2.1 The Philosophical Connection; 1.2.2 Recent Definition in Computer Science; 1.2.3 Origins of Bio-Ontologies; 1.2.4 Clinical and Medical Terminologies; 1.2.5 Recent Advances in Computer Science; 1.3 Form and Function of Ontologies; 1.3.1 Basic Components of Ontologies; 1.3.2 Components for Humans, Components for Computers; 1.3.3 Ontology Engineering; 1.4 Encoding Ontologies; 1.4.1 The OBO Format and the OBO Consortium | |
| 505 | 8 | |a 1.4.2 OBO-Edit-The Open Biomedical Ontologies Editor 1.4.3 OWL and RDF/XML; 1.4.4 Protégé-An OWL Ontology Editor; 1.5 Spotlight on GO and UMLS; 1.5.1 The Gene Ontology; 1.5.2 The Unified Medical Language System; 1.6 Types and Examples of Ontologies; 1.6.1 Upper Ontologies; 1.6.2 Domain Ontologies; 1.6.3 Formal Ontologies; 1.6.4 Informal Ontologies; 1.6.5 Reference Ontologies; 1.6.6 Application Ontologies; 1.6.7 Bio-Ontologies; 1.7 Conclusion; References; Chapter 2 Ontological Similarity Measures; 2.1 Introduction; 2.1.1 History; 2.1.2 Tversky's Parameterized Ratio Model of Similarity | |
| 505 | 8 | |a 2.1.3 Aggregation in Similarity Assessment 2.2 Traditional Approaches to Ontological Similarity; 2.2.1 Path-Based Measures; 2.2.2 Information Content Measures; 2.2.3 A Relationship Between Path-Based and Information-Content Measures; 2.3 New Approaches to Ontological Similarity; 2.3.1 Entity Class Similarity in Ontologies; 2.3.2 Cross-Ontological Similarity Measures; 2.3.3 Exploiting Common Disjunctive Ancestors; 2.4 Conclusion; References; Chapter 3 Clustering with Ontologies; 3.1 Introduction; 3.2 Relational Fuzzy C-Means (NERFCM); 3.3 Correlation Cluster Validity (CCV) | |
| 505 | 8 | |a 3.4 Ontological SOM (OSOM)3.5 Examples of NERFCM, CCV, and OSOM Applications; 3.5.1 Test Dataset; 3.5.2 Clustering of the GPD 194 Dataset Using NERFCM; 3.5.3 Determining the Number of Clusters of GPD 194 Dataset Using CCV; 3.5.4 GPD 194 Analysis Using OSOM; 3.6 Conclusion; References; Chapter 4 Analyzing and Classifying Protein Family Data Using OWL Reasoning; 4.1 Introduction; 4.1.1 Analyzing Sequence Data; 4.1.2 The Protein Phosphatase Family; 4.2 Methods; 4.2.1 The Phosphatase Classification Pipeline; 4.2.2 The Datasets; 4.2.3 The Phosphatase Ontology; 4.3 Results | |
| 505 | 8 | |a 4.3.1 Protein Phosphatases in Humans 4.3.2 Results from the Analysis of A. Fumigatus; 4.3.3 Ontology System Versus A. Fumigatus Automated Annotation Pipeline; 4.4 Ontology Classification in the Comparative Analysis of Three Protozoan Parasites-A Case Study; 4.4.1 TriTryps Diseases; 4.4.2 TriTryps Protein Phosphatases; 4.4.3 Methods for the Protozoan Parasites; 4.4.4 Sequence Analysis Results from the TriTryps Phosphatome Study; 4.4.5 Evaluation of the Ontology Classification Method; 4.5 Conclusion; References; Chapter 5 GO-Based Gene Function and Network Characterization; 5.1 Introduction | |
| 650 | 0 | |a Medical informatics. |0 http://id.loc.gov/authorities/subjects/sh89005069 | |
| 650 | 0 | |a Bioinformatics. |0 http://id.loc.gov/authorities/subjects/sh00003585 | |
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| 650 | 6 | |a Médecine |x Informatique. | |
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| 700 | 1 | |a Popescu, Mihail, |d 1962- |1 https://id.oclc.org/worldcat/entity/E39PCjK4MP6HkRpjVkp8Hkm7xP |0 http://id.loc.gov/authorities/names/no2009189084 | |
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| contents | Data Mining in Biomedicine Using Ontologies; Contents; Foreword; Preface; Chapter 1 Introduction to Ontologies; 1.1 Introduction; 1.2 History of Ontologies in Biomedicine; 1.2.1 The Philosophical Connection; 1.2.2 Recent Definition in Computer Science; 1.2.3 Origins of Bio-Ontologies; 1.2.4 Clinical and Medical Terminologies; 1.2.5 Recent Advances in Computer Science; 1.3 Form and Function of Ontologies; 1.3.1 Basic Components of Ontologies; 1.3.2 Components for Humans, Components for Computers; 1.3.3 Ontology Engineering; 1.4 Encoding Ontologies; 1.4.1 The OBO Format and the OBO Consortium 1.4.2 OBO-Edit-The Open Biomedical Ontologies Editor 1.4.3 OWL and RDF/XML; 1.4.4 Protégé-An OWL Ontology Editor; 1.5 Spotlight on GO and UMLS; 1.5.1 The Gene Ontology; 1.5.2 The Unified Medical Language System; 1.6 Types and Examples of Ontologies; 1.6.1 Upper Ontologies; 1.6.2 Domain Ontologies; 1.6.3 Formal Ontologies; 1.6.4 Informal Ontologies; 1.6.5 Reference Ontologies; 1.6.6 Application Ontologies; 1.6.7 Bio-Ontologies; 1.7 Conclusion; References; Chapter 2 Ontological Similarity Measures; 2.1 Introduction; 2.1.1 History; 2.1.2 Tversky's Parameterized Ratio Model of Similarity 2.1.3 Aggregation in Similarity Assessment 2.2 Traditional Approaches to Ontological Similarity; 2.2.1 Path-Based Measures; 2.2.2 Information Content Measures; 2.2.3 A Relationship Between Path-Based and Information-Content Measures; 2.3 New Approaches to Ontological Similarity; 2.3.1 Entity Class Similarity in Ontologies; 2.3.2 Cross-Ontological Similarity Measures; 2.3.3 Exploiting Common Disjunctive Ancestors; 2.4 Conclusion; References; Chapter 3 Clustering with Ontologies; 3.1 Introduction; 3.2 Relational Fuzzy C-Means (NERFCM); 3.3 Correlation Cluster Validity (CCV) 3.4 Ontological SOM (OSOM)3.5 Examples of NERFCM, CCV, and OSOM Applications; 3.5.1 Test Dataset; 3.5.2 Clustering of the GPD 194 Dataset Using NERFCM; 3.5.3 Determining the Number of Clusters of GPD 194 Dataset Using CCV; 3.5.4 GPD 194 Analysis Using OSOM; 3.6 Conclusion; References; Chapter 4 Analyzing and Classifying Protein Family Data Using OWL Reasoning; 4.1 Introduction; 4.1.1 Analyzing Sequence Data; 4.1.2 The Protein Phosphatase Family; 4.2 Methods; 4.2.1 The Phosphatase Classification Pipeline; 4.2.2 The Datasets; 4.2.3 The Phosphatase Ontology; 4.3 Results 4.3.1 Protein Phosphatases in Humans 4.3.2 Results from the Analysis of A. Fumigatus; 4.3.3 Ontology System Versus A. Fumigatus Automated Annotation Pipeline; 4.4 Ontology Classification in the Comparative Analysis of Three Protozoan Parasites-A Case Study; 4.4.1 TriTryps Diseases; 4.4.2 TriTryps Protein Phosphatases; 4.4.3 Methods for the Protozoan Parasites; 4.4.4 Sequence Analysis Results from the TriTryps Phosphatome Study; 4.4.5 Evaluation of the Ontology Classification Method; 4.5 Conclusion; References; Chapter 5 GO-Based Gene Function and Network Characterization; 5.1 Introduction |
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| id | ZDB-4-EBA-ocn556187536 |
| illustrated | Illustrated |
| indexdate | 2025-04-11T08:36:32Z |
| institution | BVB |
| isbn | 9781596933712 1596933712 |
| language | English |
| oclc_num | 556187536 |
| open_access_boolean | |
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| physical | 1 online resource (xv, 262 pages) : illustrations. |
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| publishDate | 2009 |
| publishDateSearch | 2009 |
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| publisher | Artech House, |
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| series | Artech House bioinformatics & biomedical imaging series. |
| series2 | Artech House series bioinformatics & biomedical imaging |
| spelling | Data mining in biomedicine using ontologies / Mihail Popescu, Dong Xu, editors. Norwood, MA : Artech House, ©2009. 1 online resource (xv, 262 pages) : illustrations. text txt rdacontent computer c rdamedia online resource cr rdacarrier data file Artech House series bioinformatics & biomedical imaging Includes bibliographical references and index. Print version record. 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. Publisher abstract. Data Mining in Biomedicine Using Ontologies; Contents; Foreword; Preface; Chapter 1 Introduction to Ontologies; 1.1 Introduction; 1.2 History of Ontologies in Biomedicine; 1.2.1 The Philosophical Connection; 1.2.2 Recent Definition in Computer Science; 1.2.3 Origins of Bio-Ontologies; 1.2.4 Clinical and Medical Terminologies; 1.2.5 Recent Advances in Computer Science; 1.3 Form and Function of Ontologies; 1.3.1 Basic Components of Ontologies; 1.3.2 Components for Humans, Components for Computers; 1.3.3 Ontology Engineering; 1.4 Encoding Ontologies; 1.4.1 The OBO Format and the OBO Consortium 1.4.2 OBO-Edit-The Open Biomedical Ontologies Editor 1.4.3 OWL and RDF/XML; 1.4.4 Protégé-An OWL Ontology Editor; 1.5 Spotlight on GO and UMLS; 1.5.1 The Gene Ontology; 1.5.2 The Unified Medical Language System; 1.6 Types and Examples of Ontologies; 1.6.1 Upper Ontologies; 1.6.2 Domain Ontologies; 1.6.3 Formal Ontologies; 1.6.4 Informal Ontologies; 1.6.5 Reference Ontologies; 1.6.6 Application Ontologies; 1.6.7 Bio-Ontologies; 1.7 Conclusion; References; Chapter 2 Ontological Similarity Measures; 2.1 Introduction; 2.1.1 History; 2.1.2 Tversky's Parameterized Ratio Model of Similarity 2.1.3 Aggregation in Similarity Assessment 2.2 Traditional Approaches to Ontological Similarity; 2.2.1 Path-Based Measures; 2.2.2 Information Content Measures; 2.2.3 A Relationship Between Path-Based and Information-Content Measures; 2.3 New Approaches to Ontological Similarity; 2.3.1 Entity Class Similarity in Ontologies; 2.3.2 Cross-Ontological Similarity Measures; 2.3.3 Exploiting Common Disjunctive Ancestors; 2.4 Conclusion; References; Chapter 3 Clustering with Ontologies; 3.1 Introduction; 3.2 Relational Fuzzy C-Means (NERFCM); 3.3 Correlation Cluster Validity (CCV) 3.4 Ontological SOM (OSOM)3.5 Examples of NERFCM, CCV, and OSOM Applications; 3.5.1 Test Dataset; 3.5.2 Clustering of the GPD 194 Dataset Using NERFCM; 3.5.3 Determining the Number of Clusters of GPD 194 Dataset Using CCV; 3.5.4 GPD 194 Analysis Using OSOM; 3.6 Conclusion; References; Chapter 4 Analyzing and Classifying Protein Family Data Using OWL Reasoning; 4.1 Introduction; 4.1.1 Analyzing Sequence Data; 4.1.2 The Protein Phosphatase Family; 4.2 Methods; 4.2.1 The Phosphatase Classification Pipeline; 4.2.2 The Datasets; 4.2.3 The Phosphatase Ontology; 4.3 Results 4.3.1 Protein Phosphatases in Humans 4.3.2 Results from the Analysis of A. Fumigatus; 4.3.3 Ontology System Versus A. Fumigatus Automated Annotation Pipeline; 4.4 Ontology Classification in the Comparative Analysis of Three Protozoan Parasites-A Case Study; 4.4.1 TriTryps Diseases; 4.4.2 TriTryps Protein Phosphatases; 4.4.3 Methods for the Protozoan Parasites; 4.4.4 Sequence Analysis Results from the TriTryps Phosphatome Study; 4.4.5 Evaluation of the Ontology Classification Method; 4.5 Conclusion; References; Chapter 5 GO-Based Gene Function and Network Characterization; 5.1 Introduction Medical informatics. http://id.loc.gov/authorities/subjects/sh89005069 Bioinformatics. http://id.loc.gov/authorities/subjects/sh00003585 Data mining. http://id.loc.gov/authorities/subjects/sh97002073 Ontologies (Information retrieval) http://id.loc.gov/authorities/subjects/sh2005006014 Computational Biology Data Mining https://id.nlm.nih.gov/mesh/D057225 Médecine Informatique. Bio-informatique. Exploration de données (Informatique) Ontologies (Recherche de l'information) TECHNOLOGY & ENGINEERING Biomedical. bisacsh MEDICAL Family & General Practice. bisacsh MEDICAL Allied Health Services Medical Technology. bisacsh MEDICAL Biotechnology. bisacsh MEDICAL Lasers in Medicine. bisacsh Bioinformatics fast Data mining fast Medical informatics fast Ontologies (Information retrieval) fast Popescu, Mihail, 1962- https://id.oclc.org/worldcat/entity/E39PCjK4MP6HkRpjVkp8Hkm7xP http://id.loc.gov/authorities/names/no2009189084 Xu, Dong, 1965- https://id.oclc.org/worldcat/entity/E39PCjJJCRH8Cyrww97ggqKhf3 http://id.loc.gov/authorities/names/n2007014632 has work: Data mining in biomedicine using ontologies (Text) https://id.oclc.org/worldcat/entity/E39PCFDD9CkC77TrYydCrdHbq3 https://id.oclc.org/worldcat/ontology/hasWork Print version: Data mining in biomedicine using ontologies. Norwood, MA : Artech House, ©2009 9781596933705 (OCoLC)326505676 Artech House bioinformatics & biomedical imaging series. http://id.loc.gov/authorities/names/no2007044458 |
| spellingShingle | Data mining in biomedicine using ontologies / Artech House bioinformatics & biomedical imaging series. Data Mining in Biomedicine Using Ontologies; Contents; Foreword; Preface; Chapter 1 Introduction to Ontologies; 1.1 Introduction; 1.2 History of Ontologies in Biomedicine; 1.2.1 The Philosophical Connection; 1.2.2 Recent Definition in Computer Science; 1.2.3 Origins of Bio-Ontologies; 1.2.4 Clinical and Medical Terminologies; 1.2.5 Recent Advances in Computer Science; 1.3 Form and Function of Ontologies; 1.3.1 Basic Components of Ontologies; 1.3.2 Components for Humans, Components for Computers; 1.3.3 Ontology Engineering; 1.4 Encoding Ontologies; 1.4.1 The OBO Format and the OBO Consortium 1.4.2 OBO-Edit-The Open Biomedical Ontologies Editor 1.4.3 OWL and RDF/XML; 1.4.4 Protégé-An OWL Ontology Editor; 1.5 Spotlight on GO and UMLS; 1.5.1 The Gene Ontology; 1.5.2 The Unified Medical Language System; 1.6 Types and Examples of Ontologies; 1.6.1 Upper Ontologies; 1.6.2 Domain Ontologies; 1.6.3 Formal Ontologies; 1.6.4 Informal Ontologies; 1.6.5 Reference Ontologies; 1.6.6 Application Ontologies; 1.6.7 Bio-Ontologies; 1.7 Conclusion; References; Chapter 2 Ontological Similarity Measures; 2.1 Introduction; 2.1.1 History; 2.1.2 Tversky's Parameterized Ratio Model of Similarity 2.1.3 Aggregation in Similarity Assessment 2.2 Traditional Approaches to Ontological Similarity; 2.2.1 Path-Based Measures; 2.2.2 Information Content Measures; 2.2.3 A Relationship Between Path-Based and Information-Content Measures; 2.3 New Approaches to Ontological Similarity; 2.3.1 Entity Class Similarity in Ontologies; 2.3.2 Cross-Ontological Similarity Measures; 2.3.3 Exploiting Common Disjunctive Ancestors; 2.4 Conclusion; References; Chapter 3 Clustering with Ontologies; 3.1 Introduction; 3.2 Relational Fuzzy C-Means (NERFCM); 3.3 Correlation Cluster Validity (CCV) 3.4 Ontological SOM (OSOM)3.5 Examples of NERFCM, CCV, and OSOM Applications; 3.5.1 Test Dataset; 3.5.2 Clustering of the GPD 194 Dataset Using NERFCM; 3.5.3 Determining the Number of Clusters of GPD 194 Dataset Using CCV; 3.5.4 GPD 194 Analysis Using OSOM; 3.6 Conclusion; References; Chapter 4 Analyzing and Classifying Protein Family Data Using OWL Reasoning; 4.1 Introduction; 4.1.1 Analyzing Sequence Data; 4.1.2 The Protein Phosphatase Family; 4.2 Methods; 4.2.1 The Phosphatase Classification Pipeline; 4.2.2 The Datasets; 4.2.3 The Phosphatase Ontology; 4.3 Results 4.3.1 Protein Phosphatases in Humans 4.3.2 Results from the Analysis of A. Fumigatus; 4.3.3 Ontology System Versus A. Fumigatus Automated Annotation Pipeline; 4.4 Ontology Classification in the Comparative Analysis of Three Protozoan Parasites-A Case Study; 4.4.1 TriTryps Diseases; 4.4.2 TriTryps Protein Phosphatases; 4.4.3 Methods for the Protozoan Parasites; 4.4.4 Sequence Analysis Results from the TriTryps Phosphatome Study; 4.4.5 Evaluation of the Ontology Classification Method; 4.5 Conclusion; References; Chapter 5 GO-Based Gene Function and Network Characterization; 5.1 Introduction Medical informatics. http://id.loc.gov/authorities/subjects/sh89005069 Bioinformatics. http://id.loc.gov/authorities/subjects/sh00003585 Data mining. http://id.loc.gov/authorities/subjects/sh97002073 Ontologies (Information retrieval) http://id.loc.gov/authorities/subjects/sh2005006014 Computational Biology Data Mining https://id.nlm.nih.gov/mesh/D057225 Médecine Informatique. Bio-informatique. Exploration de données (Informatique) Ontologies (Recherche de l'information) TECHNOLOGY & ENGINEERING Biomedical. bisacsh MEDICAL Family & General Practice. bisacsh MEDICAL Allied Health Services Medical Technology. bisacsh MEDICAL Biotechnology. bisacsh MEDICAL Lasers in Medicine. bisacsh Bioinformatics fast Data mining fast Medical informatics fast Ontologies (Information retrieval) fast |
| subject_GND | http://id.loc.gov/authorities/subjects/sh89005069 http://id.loc.gov/authorities/subjects/sh00003585 http://id.loc.gov/authorities/subjects/sh97002073 http://id.loc.gov/authorities/subjects/sh2005006014 https://id.nlm.nih.gov/mesh/D057225 |
| title | Data mining in biomedicine using ontologies / |
| title_auth | Data mining in biomedicine using ontologies / |
| title_exact_search | Data mining in biomedicine using ontologies / |
| title_full | Data mining in biomedicine using ontologies / Mihail Popescu, Dong Xu, editors. |
| title_fullStr | Data mining in biomedicine using ontologies / Mihail Popescu, Dong Xu, editors. |
| title_full_unstemmed | Data mining in biomedicine using ontologies / Mihail Popescu, Dong Xu, editors. |
| title_short | Data mining in biomedicine using ontologies / |
| title_sort | data mining in biomedicine using ontologies |
| topic | Medical informatics. http://id.loc.gov/authorities/subjects/sh89005069 Bioinformatics. http://id.loc.gov/authorities/subjects/sh00003585 Data mining. http://id.loc.gov/authorities/subjects/sh97002073 Ontologies (Information retrieval) http://id.loc.gov/authorities/subjects/sh2005006014 Computational Biology Data Mining https://id.nlm.nih.gov/mesh/D057225 Médecine Informatique. Bio-informatique. Exploration de données (Informatique) Ontologies (Recherche de l'information) TECHNOLOGY & ENGINEERING Biomedical. bisacsh MEDICAL Family & General Practice. bisacsh MEDICAL Allied Health Services Medical Technology. bisacsh MEDICAL Biotechnology. bisacsh MEDICAL Lasers in Medicine. bisacsh Bioinformatics fast Data mining fast Medical informatics fast Ontologies (Information retrieval) fast |
| topic_facet | Medical informatics. Bioinformatics. Data mining. Ontologies (Information retrieval) Computational Biology Data Mining Médecine Informatique. Bio-informatique. Exploration de données (Informatique) Ontologies (Recherche de l'information) TECHNOLOGY & ENGINEERING Biomedical. MEDICAL Family & General Practice. MEDICAL Allied Health Services Medical Technology. MEDICAL Biotechnology. MEDICAL Lasers in Medicine. Bioinformatics Data mining Medical informatics |
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