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Data Management of Protein Interaction Networks [Kõva köide]

(University of Catanzaro, Italy), Series edited by (University of Western Australia), (University of Catanzaro, Italy), Series edited by (Department of Computer Science, Georgia State University)
  • Formaat: Hardback, 216 pages, kõrgus x laius x paksus: 236x161x18 mm, kaal: 472 g, Photos: 10 B&W, 0 Color; Drawings: 100 B&W, 0 Color
  • Sari: Wiley Series in Bioinformatics
  • Ilmumisaeg: 27-Dec-2011
  • Kirjastus: John Wiley & Sons Inc
  • ISBN-10: 0470770406
  • ISBN-13: 9780470770405
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  • Kõva köide
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Data Management of Protein Interaction NetworksSuurem pilt
  • Formaat: Hardback, 216 pages, kõrgus x laius x paksus: 236x161x18 mm, kaal: 472 g, Photos: 10 B&W, 0 Color; Drawings: 100 B&W, 0 Color
  • Sari: Wiley Series in Bioinformatics
  • Ilmumisaeg: 27-Dec-2011
  • Kirjastus: John Wiley & Sons Inc
  • ISBN-10: 0470770406
  • ISBN-13: 9780470770405
Teised raamatud teemal:
Püsilink: https://www.kriso.ee/db/9780470770405.html
Märksõnad:
"Current PPI databases do not offer sophisticated querying interfaces and especially do not integrate existing information about proteins. Current algorithms for PIN analysis use only topological information, while emerging approaches attempt to exploit the biological knowledge related to proteins and kinds of interaction, e.g. protein function, localization, structure, described in Gene Ontology or PDB. The book discusses technologies, standards and databases for, respectively, generating, representingand storing PPI data. It also describes main algorithms and tools for the analysis, comparison and knowledge extraction from PINs. Moreover, some case studies and applications of PINs are also discussed"--

"Current PPI databases do not offer sophisticated querying interfaces and especially do not integrate existing information about proteins. Current algorithms for PIN analysis use only topological information, while emerging approaches attempt to exploit the biological knowledge related to proteins and kinds of interaction, e.g. protein function, localization, structure, described in Gene Ontology or PDB. The book discusses technologies, standards and databases for, respectively, generating, representing and storing PPI data. It also describes main algorithms and tools for the analysis, comparison and knowledge extraction from PINs. Moreover, some case studies and applications of PINs are also discussed"--



Current PPI databases do not offer sophisticated querying interfaces and especially do not integrate existing information about proteins. Current algorithms for PIN analysis use only topological information, while emerging approaches attempt to exploit the biological knowledge related to proteins and kinds of interaction, e.g. protein function, localization, structure, described in Gene Ontology or PDB. The book discusses technologies, standards and databases for, respectively, generating, representing and storing PPI data. It also describes main algorithms and tools for the analysis, comparison and knowledge extraction from PINs. Moreover, some case studies and applications of PINs are also discussed.

Arvustused

The material is suitable for researchers, practitioners, and graduate students in bioinformatics, molecular biology, biomedicine, and biotechnology.  (Book News, 1 April 2012)

List of Figures
xiii
List of Tables
xix
Foreword xxi
Preface xxiii
Acknowledgments xxix
Introduction xxxi
Acronyms xxxiii
1 Interactomics
1(12)
1.1 Interactomics and Omics Sciences
1(3)
1.2 Genomics and Proteomics
4(1)
1.3 Representation and Management of Protein Interaction Data
5(1)
1.4 Analysis of Protein Interaction Networks
5(1)
1.5 Visualization of Protein Interaction Networks
6(1)
1.6 Models for Biological Networks
7(1)
1.7 Flow of Information in Interactomics
8(2)
1.8 Applications of Interactomics in Biology and Medicine
10(1)
1.9 Summary
11(2)
2 Technologies for Discovering Protein Interactions
13(8)
2.1 Introduction
13(1)
2.2 Techniques Investigating Physical Interactions
14(3)
2.3 Technologies Investigating Kinetic Dynamics
17(1)
2.4 Summary
18(3)
3 Graph Theory and Applications
21(12)
3.1 Introduction
21(1)
3.2 Graph Data Structures
22(6)
3.3 Graph-Based Problems and Algorithms
28(3)
3.4 Summary
31(2)
4 Protein-To-Protein Interaction Data
33(10)
4.1 Introduction
33(1)
4.2 HUPO PSI-MI
34(7)
4.3 Summary
41(2)
5 Protein-To-Protein Interaction Databases
43(28)
5.1 Introduction
43(2)
5.2 Databases of Experimentally Determined Interactions
45(10)
5.3 Databases of Predicted Interactions
55(7)
5.4 Metadatabases: Integration of PPI Databases
62(8)
5.5 Summary
70(1)
6 Models for Protein Interaction Networks
71(8)
6.1 Introduction
71(1)
6.2 Random Graph Model
72(1)
6.3 Scale-Free Model
73(1)
6.4 Geometric Random Graph Model
73(1)
6.5 Stickiness Index (STICKY) Model
74(1)
6.6 Degree-Weighted Model
74(1)
6.7 Network Scoring Models
75(1)
6.8 Summary
76(3)
7 Algorithms Analyzing Features of Protein Interaction Networks
79(22)
7.1 Introduction
79(1)
7.2 Analysis of Protein Interaction Networks through Centrality Measures
80(1)
7.3 Extraction of Network Motifs
81(7)
7.4 Individuation of Protein Complexes
88(11)
7.5 Summary
99(2)
8 Algorithms Comparing Protein Interaction Networks
101(12)
8.1 Introduction
101(3)
8.2 Local Alignment Algorithms
104(5)
8.3 Global Alignment Algorithms
109(2)
8.4 Summary
111(2)
9 Ontology-Based Analysis of Protein Interaction Networks
113(12)
9.1 Definition of Ontology
113(2)
9.2 Languages for Modeling Ontologies
115(1)
9.3 Biomedical Ontologies
116(1)
9.4 Ontology-Based Analysis of Protein Interaction Data
117(3)
9.5 Semantic Similarity Measures of Proteins
120(2)
9.6 The Gene Ontology Annotation Database (GOA)
122(1)
9.7 FussiMeg and ProteinOn
123(1)
9.8 Summary
123(2)
10 Visualization of Protein Interaction Networks
125(16)
10.1 Introduction
125(1)
10.2 Cytoscape
126(1)
10.3 CytoMCL
127(1)
10.4 NAViGaTOR
128(2)
10.5 BioLayout Express3D
130(1)
10.6 Medusa
130(1)
10.7 ProViz
131(1)
10.8 Ondex
132(1)
10.9 PIVOT
132(1)
10.10 Pajek
133(1)
10.11 Graphviz
134(1)
10.12 GraphCrunch
134(1)
10.13 VisANT
135(1)
10.14 PIANA
136(1)
10.15 Osprey
136(1)
10.16 cPATH
137(1)
10.17 PATIKA
138(1)
10.18 Summary
139(2)
11 Case Studies in Biology and Bioinformatics
141(10)
11.1 Analysis of an Interaction Network from Proteomic Data
141(2)
11.2 Experimental Comparison of Two Interaction Networks
143(2)
11.3 Ontology-Based Management of PIN (OntoPIN)
145(4)
11.4 Ontology-Based Prediction of Protein Complexes
149(2)
12 Future Trends
151(6)
References 157(20)
Index 177
MARIO CANNATARO, PhD, is Associate Professor of Computer Engineering at the Magna Græcia University of Catanzaro. His research explores bioinformatics, computational proteomics and genomics, medical informatics, grid and parallel computing, and adaptive web systems. Dr. Cannataro has published three books and more than 150 papers in international journals and conference proceedings. PIETRO HIRAM GUZZI, PhD, is Assistant Professor of Computer Engineering at the Magna Græcia University of Catanzaro. His research focuses on the analysis of protein interaction networks and the use of biological knowledge encoded in ontologies for modeling, querying, and analyzing protein interaction networks.