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Advances in network complexity / / edited by Matthias Dehmer, Abbe Mowshowitz and Frank Emmert-Streib
| Advances in network complexity / / edited by Matthias Dehmer, Abbe Mowshowitz and Frank Emmert-Streib |
| Pubbl/distr/stampa | Weinheim, : Wiley-Blackwell, c2013 |
| Descrizione fisica | 1 online resource (xiv, 293 pages) : illustrations |
| Disciplina | 003.72 |
| Collana | Quantitative and network biology |
| Soggetto topico |
System analysis
Computational complexity Network analysis (Planning) - Mathematical models Graph theory |
| ISBN |
3-527-67048-3
3-527-67046-7 3-527-67047-5 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Advances in Network Complexity; Contents; Preface; List of Contributors; 1 Functional Complexity Based on Topology; 1.1 Introduction; 1.2 A Measure for the Functional Complexity of Networks; 1.2.1 Topological Equivalence of LCE-Graphs; 1.2.2 Vertex Resolution Patterns; 1.2.3 Kauffman States for Link Invariants; 1.2.4 Definition of the Complexity Measure; 1.3 Applications; 1.3.1 Creation of a Loop; 1.3.2 Networks of Information; 1.3.3 Transport Networks of Cargo; 1.3.4 Boolean Networks of Gene Regulation; 1.3.5 Topological Quantum Systems; 1.3.6 Steering Dynamics Stored in Knots and Links
1.4 ConclusionsReferences; 2 Connections Between Artificial Intelligence and Computational Complexity and the Complexity of Graphs; 2.1 Introduction; 2.2 Representation Methods; 2.3 Searching Methods; 2.4 Turing Machines; 2.5 Fuzzy Logic and Fuzzy Graphs; 2.6 Fuzzy Optimization; 2.7 Fuzzy Systems; 2.8 Problems Related to AI; 2.9 Topology of Complex Networks; 2.10 Hierarchies; 2.10.1 Deterministic Case; 2.10.2 Nondeterministic Case; 2.10.3 Alternating Case; 2.11 Graph Entropy; 2.12 Kolmogorov Complexity; 2.13 Conclusion; References 3 Selection-Based Estimates of Complexity Unravel Some Mechanisms and Selective Pressures Underlying the Evolution of Complexity in Artificial Networks3.1 Introduction; 3.2 Complexity and Evolution; 3.3 Macroscopic Quantification of Organismal Complexity; 3.4 Selection-Based Methods of Complexity; 3.5 Informational Complexity; 3.6 Fisher Geometric Model; 3.7 The Cost of Complexity; 3.8 Quantifying Phenotypic Complexity; 3.8.1 Mutation-Based Method: Mutational Phenotypic Complexity (MPC); 3.8.2 Drift Load Based Method: Effective Phenotypic Complexity (EPC) 3.8.3 Statistical Method: Principal Component Phenotypic Complexity (PCPC)3.9 Darwinian Adaptive Neural Networks (DANN); 3.10 The Different Facets of Complexity; 3.11 Mechanistic Understanding of Phenotypic Complexity; 3.12 Selective Pressures Acting on Phenotypic Complexity; 3.13 Conclusion and Perspectives; References; 4 Three Types of Network Complexity Pyramid; 4.1 Introduction; 4.2 The First Type: The Life's Complexity Pyramid (LCP); 4.3 The Second Type: Network Model Complexity Pyramid; 4.3.1 The Level-7: Euler (Regular) Graphs; 4.3.2 The Level-6: Erd€os-R enyi Random Graph 4.3.3 The Level-5: Small-World Network and Scale-Free Models4.3.4 The Level-4: Weighted Evolving Network Models; 4.3.5 The Bottom Three Levels of the NMCP; 4.3.5.1 The Level-3: The HUHPNM; 4.3.5.2 The Level-2: The LUHNM; 4.3.5.3 The Level-1: The LUHNM-VSG; 4.4 The Third Type: Generalized Farey Organized Network Pyramid; 4.4.1 Construction Method of the Generalized Farey Tree Network (GFTN); 4.4.2 Main Results of the GFTN; 4.4.2.1 Degree Distribution; 4.4.2.2 Clustering Coefficient; 4.4.2.3 Diameter and Small World; 4.4.2.4 Degree-Degree Correlations; 4.4.3 Weighted Property of GFTN 4.4.4 Generalized Farey Organized Network Pyramid (GFONP) |
| Record Nr. | UNINA-9910139042303321 |
| Weinheim, : Wiley-Blackwell, c2013 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Advances in network complexity / / edited by Matthias Dehmer, Abbe Mowshowitz and Frank Emmert-Streib
| Advances in network complexity / / edited by Matthias Dehmer, Abbe Mowshowitz and Frank Emmert-Streib |
| Pubbl/distr/stampa | Weinheim, : Wiley-Blackwell, c2013 |
| Descrizione fisica | 1 online resource (xiv, 293 pages) : illustrations |
| Disciplina | 003.72 |
| Collana | Quantitative and network biology |
| Soggetto topico |
System analysis
Computational complexity Network analysis (Planning) - Mathematical models Graph theory |
| ISBN |
3-527-67048-3
3-527-67046-7 3-527-67047-5 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Advances in Network Complexity; Contents; Preface; List of Contributors; 1 Functional Complexity Based on Topology; 1.1 Introduction; 1.2 A Measure for the Functional Complexity of Networks; 1.2.1 Topological Equivalence of LCE-Graphs; 1.2.2 Vertex Resolution Patterns; 1.2.3 Kauffman States for Link Invariants; 1.2.4 Definition of the Complexity Measure; 1.3 Applications; 1.3.1 Creation of a Loop; 1.3.2 Networks of Information; 1.3.3 Transport Networks of Cargo; 1.3.4 Boolean Networks of Gene Regulation; 1.3.5 Topological Quantum Systems; 1.3.6 Steering Dynamics Stored in Knots and Links
1.4 ConclusionsReferences; 2 Connections Between Artificial Intelligence and Computational Complexity and the Complexity of Graphs; 2.1 Introduction; 2.2 Representation Methods; 2.3 Searching Methods; 2.4 Turing Machines; 2.5 Fuzzy Logic and Fuzzy Graphs; 2.6 Fuzzy Optimization; 2.7 Fuzzy Systems; 2.8 Problems Related to AI; 2.9 Topology of Complex Networks; 2.10 Hierarchies; 2.10.1 Deterministic Case; 2.10.2 Nondeterministic Case; 2.10.3 Alternating Case; 2.11 Graph Entropy; 2.12 Kolmogorov Complexity; 2.13 Conclusion; References 3 Selection-Based Estimates of Complexity Unravel Some Mechanisms and Selective Pressures Underlying the Evolution of Complexity in Artificial Networks3.1 Introduction; 3.2 Complexity and Evolution; 3.3 Macroscopic Quantification of Organismal Complexity; 3.4 Selection-Based Methods of Complexity; 3.5 Informational Complexity; 3.6 Fisher Geometric Model; 3.7 The Cost of Complexity; 3.8 Quantifying Phenotypic Complexity; 3.8.1 Mutation-Based Method: Mutational Phenotypic Complexity (MPC); 3.8.2 Drift Load Based Method: Effective Phenotypic Complexity (EPC) 3.8.3 Statistical Method: Principal Component Phenotypic Complexity (PCPC)3.9 Darwinian Adaptive Neural Networks (DANN); 3.10 The Different Facets of Complexity; 3.11 Mechanistic Understanding of Phenotypic Complexity; 3.12 Selective Pressures Acting on Phenotypic Complexity; 3.13 Conclusion and Perspectives; References; 4 Three Types of Network Complexity Pyramid; 4.1 Introduction; 4.2 The First Type: The Life's Complexity Pyramid (LCP); 4.3 The Second Type: Network Model Complexity Pyramid; 4.3.1 The Level-7: Euler (Regular) Graphs; 4.3.2 The Level-6: Erd€os-R enyi Random Graph 4.3.3 The Level-5: Small-World Network and Scale-Free Models4.3.4 The Level-4: Weighted Evolving Network Models; 4.3.5 The Bottom Three Levels of the NMCP; 4.3.5.1 The Level-3: The HUHPNM; 4.3.5.2 The Level-2: The LUHNM; 4.3.5.3 The Level-1: The LUHNM-VSG; 4.4 The Third Type: Generalized Farey Organized Network Pyramid; 4.4.1 Construction Method of the Generalized Farey Tree Network (GFTN); 4.4.2 Main Results of the GFTN; 4.4.2.1 Degree Distribution; 4.4.2.2 Clustering Coefficient; 4.4.2.3 Diameter and Small World; 4.4.2.4 Degree-Degree Correlations; 4.4.3 Weighted Property of GFTN 4.4.4 Generalized Farey Organized Network Pyramid (GFONP) |
| Record Nr. | UNINA-9910815777903321 |
| Weinheim, : Wiley-Blackwell, c2013 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Analysis of complex networks [[electronic resource] ] : from biology to linguistics / / edited by Matthias Dehmer and Frank Emmert-Streib
| Analysis of complex networks [[electronic resource] ] : from biology to linguistics / / edited by Matthias Dehmer and Frank Emmert-Streib |
| Autore | Dehmer Matthias |
| Edizione | [1st edition] |
| Pubbl/distr/stampa | Weinheim, : Wiley-VCH, c2009 |
| Descrizione fisica | 1 online resource (482 p.) |
| Disciplina | 515 |
| Altri autori (Persone) |
DehmerMatthias <1968->
Emmert-StreibFrank |
| Soggetto topico |
Mathematical analysis
Information networks Graph theory |
| Soggetto genere / forma | Electronic books. |
| ISBN |
1-282-68269-5
9786612682698 3-527-62798-7 3-527-62799-5 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Analysis of Complex Networks From Biology to Linguistics; Contents; Preface; List of Contributors; 1 Entropy, Orbits, and Spectra of Graphs; 1.1 Introduction; 1.2 Entropy or the Information Content of Graphs; 1.3 Groups and Graph Spectra; 1.4 Approximating Orbits; 1.4.1 The Degree of the Vertices; 1.4.2 The Point-Deleted Neighborhood Degree Vector; 1.4.3 Betweenness Centrality; 1.5 Alternative Bases for Structural Complexity; References; 2 Statistical Mechanics of Complex Networks; 2.1 Introduction; 2.1.1 Network Entropies; 2.1.2 Network Hamiltonians; 2.1.3 Network Ensembles
2.1.4 Some Definitions of Network Measures2.2 Macroscopics: Entropies for Networks; 2.2.1 A General Set of Network Models Maximizing Generalized Entropies; 2.2.1.1 A Unified Network Model; 2.2.1.2 Famous Limits of the Unified Model; 2.2.1.3 Unified Model: Additional Features; 2.3 Microscopics: Hamiltonians of Networks - Network Thermodynamics; 2.3.1 Topological Phase Transitions; 2.3.2 A Note on Entropy; 2.4 Ensembles of Random Networks - Superstatistics; 2.5 Conclusion; References; 3 A Simple Integrated Approach to Network Complexity and Node Centrality; 3.1 Introduction 3.2 The Small-World Connectivity Descriptors3.3 The Integrated Centrality Measure; References; 4 Spectral Theory of Networks: From Biomolecular to Ecological Systems; 4.1 Introduction; 4.2 Background on Graph Spectra; 4.3 Spectral Measures of Node Centrality; 4.3.1 Subgraph Centrality as a Partition Function; 4.3.2 Application; 4.4 Global Topological Organization of Complex Networks; 4.4.1 Spectral Scaling Method; 4.4.2 Universal Topological Classes of Networks; 4.4.3 Applications; 4.5 Communicability in Complex Networks; 4.5.1 Communicability and Network Communities 4.5.2 Detection of Communities: The Communicability Graph4.5.3 Application; 4.6 Network Bipartivity; 4.6.1 Detecting Bipartite Substructures in Complex Networks; 4.6.2 Application; 4.7 Conclusion; References; 5 On the Structure of Neutral Networks of RNA Pseudoknot Structures; 5.1 Motivation and Background; 5.1.1 Notation and Terminology; 5.2 Preliminaries; 5.3 Connectivity; 5.4 The Largest Component; 5.5 Distances in n-Cubes; 5.6 Conclusion; References; 6 Graph Edit Distance - Optimal and Suboptimal Algorithms with Applications; 6.1 Introduction; 6.2 Graph Edit Distance 6.3 Computation of GED6.3.1 Optimal Algorithms; 6.3.2 Suboptimal Algorithms; 6.3.2.1 Bipartite Graph Matching; 6.4 Applications; 6.4.1 Graph Data Sets; 6.4.2 GED-Based Nearest-Neighbor Classification; 6.4.3 Dissimilarity-Based Embedding Graph Kernels; 6.5 Experimental Evaluation; 6.5.1 Optimal vs. Suboptimal Graph Edit Distance; 6.5.2 Dissimilarity Embedding Graph Kernels Based on Suboptimal Graph Edit Distance; 6.6 Summary and Conclusions; References; 7 Graph Energy; 7.1 Introduction; 7.2 Bounds for the Energy of Graphs; 7.2.1 Some Upper Bounds; 7.2.2 Some Lower Bounds 7.3 Hyperenergetic, Hypoenergetic, and Equienergetic Graphs |
| Record Nr. | UNINA-9910139802803321 |
Dehmer Matthias
|
||
| Weinheim, : Wiley-VCH, c2009 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Analysis of complex networks [[electronic resource] ] : from biology to linguistics / / edited by Matthias Dehmer and Frank Emmert-Streib
| Analysis of complex networks [[electronic resource] ] : from biology to linguistics / / edited by Matthias Dehmer and Frank Emmert-Streib |
| Autore | Dehmer Matthias |
| Edizione | [1st edition] |
| Pubbl/distr/stampa | Weinheim, : Wiley-VCH, c2009 |
| Descrizione fisica | 1 online resource (482 p.) |
| Disciplina | 515 |
| Altri autori (Persone) |
DehmerMatthias <1968->
Emmert-StreibFrank |
| Soggetto topico |
Mathematical analysis
Information networks Graph theory |
| ISBN |
1-282-68269-5
9786612682698 3-527-62798-7 3-527-62799-5 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Analysis of Complex Networks From Biology to Linguistics; Contents; Preface; List of Contributors; 1 Entropy, Orbits, and Spectra of Graphs; 1.1 Introduction; 1.2 Entropy or the Information Content of Graphs; 1.3 Groups and Graph Spectra; 1.4 Approximating Orbits; 1.4.1 The Degree of the Vertices; 1.4.2 The Point-Deleted Neighborhood Degree Vector; 1.4.3 Betweenness Centrality; 1.5 Alternative Bases for Structural Complexity; References; 2 Statistical Mechanics of Complex Networks; 2.1 Introduction; 2.1.1 Network Entropies; 2.1.2 Network Hamiltonians; 2.1.3 Network Ensembles
2.1.4 Some Definitions of Network Measures2.2 Macroscopics: Entropies for Networks; 2.2.1 A General Set of Network Models Maximizing Generalized Entropies; 2.2.1.1 A Unified Network Model; 2.2.1.2 Famous Limits of the Unified Model; 2.2.1.3 Unified Model: Additional Features; 2.3 Microscopics: Hamiltonians of Networks - Network Thermodynamics; 2.3.1 Topological Phase Transitions; 2.3.2 A Note on Entropy; 2.4 Ensembles of Random Networks - Superstatistics; 2.5 Conclusion; References; 3 A Simple Integrated Approach to Network Complexity and Node Centrality; 3.1 Introduction 3.2 The Small-World Connectivity Descriptors3.3 The Integrated Centrality Measure; References; 4 Spectral Theory of Networks: From Biomolecular to Ecological Systems; 4.1 Introduction; 4.2 Background on Graph Spectra; 4.3 Spectral Measures of Node Centrality; 4.3.1 Subgraph Centrality as a Partition Function; 4.3.2 Application; 4.4 Global Topological Organization of Complex Networks; 4.4.1 Spectral Scaling Method; 4.4.2 Universal Topological Classes of Networks; 4.4.3 Applications; 4.5 Communicability in Complex Networks; 4.5.1 Communicability and Network Communities 4.5.2 Detection of Communities: The Communicability Graph4.5.3 Application; 4.6 Network Bipartivity; 4.6.1 Detecting Bipartite Substructures in Complex Networks; 4.6.2 Application; 4.7 Conclusion; References; 5 On the Structure of Neutral Networks of RNA Pseudoknot Structures; 5.1 Motivation and Background; 5.1.1 Notation and Terminology; 5.2 Preliminaries; 5.3 Connectivity; 5.4 The Largest Component; 5.5 Distances in n-Cubes; 5.6 Conclusion; References; 6 Graph Edit Distance - Optimal and Suboptimal Algorithms with Applications; 6.1 Introduction; 6.2 Graph Edit Distance 6.3 Computation of GED6.3.1 Optimal Algorithms; 6.3.2 Suboptimal Algorithms; 6.3.2.1 Bipartite Graph Matching; 6.4 Applications; 6.4.1 Graph Data Sets; 6.4.2 GED-Based Nearest-Neighbor Classification; 6.4.3 Dissimilarity-Based Embedding Graph Kernels; 6.5 Experimental Evaluation; 6.5.1 Optimal vs. Suboptimal Graph Edit Distance; 6.5.2 Dissimilarity Embedding Graph Kernels Based on Suboptimal Graph Edit Distance; 6.6 Summary and Conclusions; References; 7 Graph Energy; 7.1 Introduction; 7.2 Bounds for the Energy of Graphs; 7.2.1 Some Upper Bounds; 7.2.2 Some Lower Bounds 7.3 Hyperenergetic, Hypoenergetic, and Equienergetic Graphs |
| Record Nr. | UNINA-9910830301503321 |
|
Dehmer Matthias
|
||
| Weinheim, : Wiley-VCH, c2009 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Analysis of complex networks [[electronic resource] ] : from biology to linguistics / / edited by Matthias Dehmer and Frank Emmert-Streib
| Analysis of complex networks [[electronic resource] ] : from biology to linguistics / / edited by Matthias Dehmer and Frank Emmert-Streib |
| Autore | Dehmer Matthias |
| Edizione | [1st edition] |
| Pubbl/distr/stampa | Weinheim, : Wiley-VCH, c2009 |
| Descrizione fisica | 1 online resource (482 p.) |
| Disciplina | 515 |
| Altri autori (Persone) |
DehmerMatthias <1968->
Emmert-StreibFrank |
| Soggetto topico |
Mathematical analysis
Information networks Graph theory |
| ISBN |
1-282-68269-5
9786612682698 3-527-62798-7 3-527-62799-5 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Analysis of Complex Networks From Biology to Linguistics; Contents; Preface; List of Contributors; 1 Entropy, Orbits, and Spectra of Graphs; 1.1 Introduction; 1.2 Entropy or the Information Content of Graphs; 1.3 Groups and Graph Spectra; 1.4 Approximating Orbits; 1.4.1 The Degree of the Vertices; 1.4.2 The Point-Deleted Neighborhood Degree Vector; 1.4.3 Betweenness Centrality; 1.5 Alternative Bases for Structural Complexity; References; 2 Statistical Mechanics of Complex Networks; 2.1 Introduction; 2.1.1 Network Entropies; 2.1.2 Network Hamiltonians; 2.1.3 Network Ensembles
2.1.4 Some Definitions of Network Measures2.2 Macroscopics: Entropies for Networks; 2.2.1 A General Set of Network Models Maximizing Generalized Entropies; 2.2.1.1 A Unified Network Model; 2.2.1.2 Famous Limits of the Unified Model; 2.2.1.3 Unified Model: Additional Features; 2.3 Microscopics: Hamiltonians of Networks - Network Thermodynamics; 2.3.1 Topological Phase Transitions; 2.3.2 A Note on Entropy; 2.4 Ensembles of Random Networks - Superstatistics; 2.5 Conclusion; References; 3 A Simple Integrated Approach to Network Complexity and Node Centrality; 3.1 Introduction 3.2 The Small-World Connectivity Descriptors3.3 The Integrated Centrality Measure; References; 4 Spectral Theory of Networks: From Biomolecular to Ecological Systems; 4.1 Introduction; 4.2 Background on Graph Spectra; 4.3 Spectral Measures of Node Centrality; 4.3.1 Subgraph Centrality as a Partition Function; 4.3.2 Application; 4.4 Global Topological Organization of Complex Networks; 4.4.1 Spectral Scaling Method; 4.4.2 Universal Topological Classes of Networks; 4.4.3 Applications; 4.5 Communicability in Complex Networks; 4.5.1 Communicability and Network Communities 4.5.2 Detection of Communities: The Communicability Graph4.5.3 Application; 4.6 Network Bipartivity; 4.6.1 Detecting Bipartite Substructures in Complex Networks; 4.6.2 Application; 4.7 Conclusion; References; 5 On the Structure of Neutral Networks of RNA Pseudoknot Structures; 5.1 Motivation and Background; 5.1.1 Notation and Terminology; 5.2 Preliminaries; 5.3 Connectivity; 5.4 The Largest Component; 5.5 Distances in n-Cubes; 5.6 Conclusion; References; 6 Graph Edit Distance - Optimal and Suboptimal Algorithms with Applications; 6.1 Introduction; 6.2 Graph Edit Distance 6.3 Computation of GED6.3.1 Optimal Algorithms; 6.3.2 Suboptimal Algorithms; 6.3.2.1 Bipartite Graph Matching; 6.4 Applications; 6.4.1 Graph Data Sets; 6.4.2 GED-Based Nearest-Neighbor Classification; 6.4.3 Dissimilarity-Based Embedding Graph Kernels; 6.5 Experimental Evaluation; 6.5.1 Optimal vs. Suboptimal Graph Edit Distance; 6.5.2 Dissimilarity Embedding Graph Kernels Based on Suboptimal Graph Edit Distance; 6.6 Summary and Conclusions; References; 7 Graph Energy; 7.1 Introduction; 7.2 Bounds for the Energy of Graphs; 7.2.1 Some Upper Bounds; 7.2.2 Some Lower Bounds 7.3 Hyperenergetic, Hypoenergetic, and Equienergetic Graphs |
| Record Nr. | UNINA-9910840815503321 |
|
Dehmer Matthias
|
||
| Weinheim, : Wiley-VCH, c2009 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Analysis of microarray data : a network-based approach / / edited by Frank Emmert-Streib and Matthias Dehmer
| Analysis of microarray data : a network-based approach / / edited by Frank Emmert-Streib and Matthias Dehmer |
| Pubbl/distr/stampa | Weinheim, [Germany] : , : Wiley-VCH Verlag GmbH & Co. KGaA, , 2008 |
| Descrizione fisica | 1 online resource (440 p.) |
| Disciplina | 572.8636 |
| Soggetto topico | DNA microarrays |
| Soggetto genere / forma | Electronic books. |
| ISBN |
1-281-94703-2
9786611947033 3-527-62281-0 3-527-62282-9 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Analysis of Microarray Data; Contents; Preface; List of Contributors; 1 Introduction to DNA Microarrays; 1.1 Introduction; 1.1.1 The Genome is an Information Scaffold; 1.1.2 Gene Expression is Detected by Hybridization; 1.1.2.1 Hybridization is Used to Measure Gene Expression; 1.1.2.2 Microarrays Provide a New Twist to an Old Technique; 1.2 Types of Arrays; 1.2.1 Spotted Microarrays; 1.2.2 Affymetrix GeneChips; 1.2.2.1 Other In Situ Synthesis Platforms; 1.2.2.2 Uses of Microarrays; 1.3 Array Content; 1.3.1 ESTs Are the First View; 1.3.1.1 Probe Design; 1.4 Normalization and Scaling
1.4.1 Be Unbiased, Be Complete1.4.2 Sequence Counts; References; 2 Comparative Analysis of Clustering Methods for Microarray Data; 2.1 Introduction; 2.2 Measuring Distance Between Genes or Clusters; 2.3 Network Models; 2.3.1 Boolean Network; 2.3.2 Coexpression Network; 2.3.3 Bayesian Network; 2.3.4 Co-Occurrence Network; 2.4 Network Constrained Clustering Method; 2.4.1 Extract the Giant Connected Component; 2.4.2 Compute "Network Constrained Distance Matrix"; 2.5 Network Constrained Clustering Results; 2.5.1 Yeast Galactose Metabolism Pathway; 2.5.2 Retinal Gene Expression Data 2.5.3 Mouse Segmentation Clock Data2.6 Discussion and Conclusion; References; 3 Finding Verified Edges in Genetic/Gene Networks: Bilayer Verification for Network Recovery in the Presence of Hidden Confounders; 3.1 Introduction: Gene and Genetic Networks; 3.2 Background and Prior Theory; 3.2.1 Motivation; 3.2.2 Bayesian Networks Theory; 3.2.2.1 d-Separation at Colliders; 3.2.2.2 Placing Genetic Tests Within the Bayesian Network Framework; 3.2.3 Learning Network Structure from Observed Conditional Independencies; 3.2.4 Prior Work: The PC Algorithm; 3.2.4.1 PC Algorithm 3.5 Results and Further Application3.5.1 Estimating α False-Positive Rates for the v-Structure Test; 3.5.2 Learning an Aortic Lesion Network; 3.5.3 Further Utilizing Networks: Assigning Functional Roles to Genes; 3.5.4 Future Work; References; 4 Computational Inference of Biological Causal Networks - Analysis of Therapeutic Compound Effects; 4.1 Introduction; 4.2 Basic Theory of Bayesian Networks; 4.2.1 Bayesian Scoring Metrics; 4.2.2 Heuristic Search Methods; 4.2.3 Inference Score; 4.3 Methods; 4.3.1 Experimental Design; 4.3.2 Tissue Contamination; 4.3.3 Gene List Prefiltering 4.3.4 Outlier Removal |
| Record Nr. | UNINA-9910144107303321 |
| Weinheim, [Germany] : , : Wiley-VCH Verlag GmbH & Co. KGaA, , 2008 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Analysis of microarray data : a network-based approach / / edited by Frank Emmert-Streib and Matthias Dehmer
| Analysis of microarray data : a network-based approach / / edited by Frank Emmert-Streib and Matthias Dehmer |
| Pubbl/distr/stampa | Weinheim, [Germany] : , : Wiley-VCH Verlag GmbH & Co. KGaA, , 2008 |
| Descrizione fisica | 1 online resource (440 p.) |
| Disciplina | 572.8636 |
| Soggetto topico | DNA microarrays |
| ISBN |
1-281-94703-2
9786611947033 3-527-62281-0 3-527-62282-9 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Analysis of Microarray Data; Contents; Preface; List of Contributors; 1 Introduction to DNA Microarrays; 1.1 Introduction; 1.1.1 The Genome is an Information Scaffold; 1.1.2 Gene Expression is Detected by Hybridization; 1.1.2.1 Hybridization is Used to Measure Gene Expression; 1.1.2.2 Microarrays Provide a New Twist to an Old Technique; 1.2 Types of Arrays; 1.2.1 Spotted Microarrays; 1.2.2 Affymetrix GeneChips; 1.2.2.1 Other In Situ Synthesis Platforms; 1.2.2.2 Uses of Microarrays; 1.3 Array Content; 1.3.1 ESTs Are the First View; 1.3.1.1 Probe Design; 1.4 Normalization and Scaling
1.4.1 Be Unbiased, Be Complete1.4.2 Sequence Counts; References; 2 Comparative Analysis of Clustering Methods for Microarray Data; 2.1 Introduction; 2.2 Measuring Distance Between Genes or Clusters; 2.3 Network Models; 2.3.1 Boolean Network; 2.3.2 Coexpression Network; 2.3.3 Bayesian Network; 2.3.4 Co-Occurrence Network; 2.4 Network Constrained Clustering Method; 2.4.1 Extract the Giant Connected Component; 2.4.2 Compute "Network Constrained Distance Matrix"; 2.5 Network Constrained Clustering Results; 2.5.1 Yeast Galactose Metabolism Pathway; 2.5.2 Retinal Gene Expression Data 2.5.3 Mouse Segmentation Clock Data2.6 Discussion and Conclusion; References; 3 Finding Verified Edges in Genetic/Gene Networks: Bilayer Verification for Network Recovery in the Presence of Hidden Confounders; 3.1 Introduction: Gene and Genetic Networks; 3.2 Background and Prior Theory; 3.2.1 Motivation; 3.2.2 Bayesian Networks Theory; 3.2.2.1 d-Separation at Colliders; 3.2.2.2 Placing Genetic Tests Within the Bayesian Network Framework; 3.2.3 Learning Network Structure from Observed Conditional Independencies; 3.2.4 Prior Work: The PC Algorithm; 3.2.4.1 PC Algorithm 3.5 Results and Further Application3.5.1 Estimating α False-Positive Rates for the v-Structure Test; 3.5.2 Learning an Aortic Lesion Network; 3.5.3 Further Utilizing Networks: Assigning Functional Roles to Genes; 3.5.4 Future Work; References; 4 Computational Inference of Biological Causal Networks - Analysis of Therapeutic Compound Effects; 4.1 Introduction; 4.2 Basic Theory of Bayesian Networks; 4.2.1 Bayesian Scoring Metrics; 4.2.2 Heuristic Search Methods; 4.2.3 Inference Score; 4.3 Methods; 4.3.1 Experimental Design; 4.3.2 Tissue Contamination; 4.3.3 Gene List Prefiltering 4.3.4 Outlier Removal |
| Record Nr. | UNINA-9910830082603321 |
| Weinheim, [Germany] : , : Wiley-VCH Verlag GmbH & Co. KGaA, , 2008 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Computational network analysis with R : applications in biology, medicine, and chemistry / / edited by Mathtias Dehmer, Yongtang Shi, and Frank Emmert-Streib
| Computational network analysis with R : applications in biology, medicine, and chemistry / / edited by Mathtias Dehmer, Yongtang Shi, and Frank Emmert-Streib |
| Pubbl/distr/stampa | Weinheim : , : Wiley-VCH, , [2017] |
| Descrizione fisica | 1 online resource (365 p.) |
| Collana | Quantitative and network biology |
| Soggetto topico |
R (Computer program language)
Medicine - Computer programs Application software |
| ISBN |
3-527-69437-4
3-527-69440-4 3-527-69436-6 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Cover; Title Page; Copyright; Contents; List of Contributors; Chapter 1 Using the DiffCorr Package to Analyze and Visualize Differential Correlations in Biological Networks; 1.1 Introduction; 1.1.1 An Introduction to Omics and Systems Biology; 1.1.2 Correlation Networks in Omics and Systems Biology; 1.1.3 Network Modules and Differential Network Approaches; 1.1.4 Aims of this Chapter; 1.2 What is DiffCorr?; 1.2.1 Background; 1.2.2 Methods; 1.2.3 Main Functions in DiffCorr; 1.2.4 Installing the DiffCorr Package
1.3 Constructing Co-Expression (Correlation) Networks from Omics Data - Transcriptome Data set1.3.1 Downloading the Transcriptome Data set; 1.3.2 Data Filtering; 1.3.3 Calculation of the Correlation and Visualization of Correlation Networks; 1.3.4 Graph Clustering; 1.3.5 Gene Ontology Enrichment Analysis; 1.4 Differential Correlation Analysis by DiffCorr Package; 1.4.1 Calculation of Differential Co-Expression between Organs in Arabidopsis; 1.4.2 Exploring the Metabolome Data of Flavonoid-Deficient Arabidopsis; 1.4.3 Avoiding Pitfalls in (Differential) Correlation Analysis; 1.5 Conclusion AcknowledgmentsConflicts of Interest; References; Chapter 2 Analytical Models and Methods for Anomaly Detection in Dynamic, Attributed Graphs; 2.1 Introduction; 2.2 Chapter Definitions and Notation; 2.3 Anomaly Detection in Graph Data; 2.3.1 Neighborhood-Based Techniques; 2.3.2 Frequent Subgraph Techniques; 2.3.3 Anomalies in Random Graphs; 2.4 Random Graph Models; 2.4.1 Models with Attributes; 2.4.2 Dynamic Graph Models; 2.5 Spectral Subgraph Detection in Dynamic, Attributed Graphs; 2.5.1 Problem Model; 2.5.2 Filter Optimization; 2.5.3 Residuals Analysis in Attributed Graphs 2.6 Implementation in R2.7 Demonstration in Random Synthetic Backgrounds; 2.8 Data Analysis Example; 2.9 Summary; Acknowledgments; References; Chapter 3 Bayesian Computational Algorithms for Social Network Analysis; 3.1 Introduction; 3.2 Social Networks as Random Graphs; 3.3 Statistical Modeling Approaches to Social Network Analysis; 3.3.1 Exponential Random Graph Models (ERGMs); 3.3.2 Latent Space Models (LSMs); 3.4 Bayesian Inference for Social Network Models; 3.4.1 R-Based Software Tools; 3.5 Data; 3.5.1 Bayesian Inference for Exponential Random Graph Models 3.5.2 Bayesian Inference for Latent Space Models3.5.3 Predictive Goodness-of-Fit (GoF) Diagnostics; 3.6 Conclusions; References; Chapter 4 Threshold Degradation in R Using iDEMO; 4.1 Introduction; 4.2 Statistical Overview: Degradation Models; 4.2.1 Wiener Degradation-Based Process; 4.2.1.1 Lifetime Information; 4.2.1.2 Log-Likelihood Function; 4.2.2 Gamma Degradation-Based Process; 4.2.2.1 Lifetime Information; 4.2.2.2 Log-Likelihood Function; 4.2.3 Inverse Gaussian Degradation-Based Process; 4.2.3.1 Lifetime Distribution; 4.2.3.2 Log-Likelihood Function; 4.2.4 Model Selection Criteria 4.2.5 Choice of (t) |
| Record Nr. | UNINA-9910134853503321 |
| Weinheim : , : Wiley-VCH, , [2017] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Computational network theory : theoretical foundations and applications / / Edited by Matthias Dehmer, Frank Emmert-Streib, and Stefan Pickl
| Computational network theory : theoretical foundations and applications / / Edited by Matthias Dehmer, Frank Emmert-Streib, and Stefan Pickl |
| Pubbl/distr/stampa | Weinheim, Germany : , : Wiley-VCH Verlang GmbH & Co. KGaA, , 2015 |
| Descrizione fisica | 1 online resource (281 p.) |
| Disciplina | 006.3 |
| Collana | Quantitative and network biology |
| Soggetto topico |
Electronic commerce
Computational intelligence |
| Soggetto genere / forma | Electronic books. |
| ISBN |
3-527-69154-5
3-527-69151-0 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Cover; Title Page; Copyright; Dedication; Contents; Color Plates; Preface; List of Contributors; Chapter 1 Model Selection for Neural Network Models: A Statistical Perspective; 1.1 Introduction; 1.2 Feedforward Neural Network Models; 1.3 Model Selection; 1.3.1 Feature Selection by Relevance Measures; 1.3.2 Some Numerical Examples; 1.3.3 Application to Real Data; 1.4 The Selection of the Hidden Layer Size; 1.4.1 A Reality Check Approach; 1.4.2 Numerical Examples by Using the Reality Check; 1.4.3 Testing Superior Predictive Ability for Neural Network Modeling
1.4.4 Some Numerical Results Using Test of Superior Predictive Ability1.4.5 An Application to Real Data; 1.5 Concluding Remarks; References; Chapter 2 Measuring Structural Correlations in Graphs; 2.1 Introduction; 2.1.1 Solutions for Measuring Structural Correlations; 2.2 Related Work; 2.3 Self Structural Correlation; 2.3.1 Problem Formulation; 2.3.2 The Measure; 2.3.2.1 Random Walk and Hitting Time; 2.3.2.2 Decayed Hitting Time; 2.3.3 Computing Decayed Hitting Time; 2.3.3.1 Iterative Approximation; 2.3.3.2 A Sampling Algorithm for h(vi,B); 2.3.3.3 Complexity; 2.3.4 Assessing SSC 2.3.4.1 Estimating ρ (Vq)2.3.4.2 Estimating the Significance of ρ (Vq); 2.3.5 Empirical Studies; 2.3.5.1 Datasets; 2.3.5.2 Performance of DHT Approximation; 2.3.5.3 Effectiveness on Synthetic Events; 2.3.5.4 SSC of Real Event; 2.3.5.5 Scalability of Sampling-alg; 2.3.6 Discussions; 2.4 Two-Event Structural Correlation; 2.4.1 Preliminaries and Problem Formulation; 2.4.2 Measuring TESC; 2.4.2.1 The Test; 2.4.2.2 Reference Nodes; 2.4.3 Reference Node Sampling; 2.4.3.1 Batch_BFS; 2.4.3.2 Importance Sampling; 2.4.3.3 Global Sampling in Whole Graph; 2.4.3.4 Complexity Analysis; 2.4.4 Experiments 2.4.4.1 Graph Datasets2.4.4.2 Event Simulation Methodology; 2.4.4.3 Performance Comparison; 2.4.4.4 Batch Importance Sampling; 2.4.4.5 Impact of Graph Density; 2.4.4.6 Efficiency and Scalability; 2.4.4.7 Real Events; 2.4.5 Discussions; 2.5 Conclusions; Acknowledgments; References; Chapter 3 Spectral Graph Theory and Structural Analysis of Complex Networks: An Introduction; 3.1 Introduction; 3.2 Graph Theory: Some Basic Concepts; 3.2.1 Connectivity in Graphs; 3.2.2 Subgraphs and Special Graphs; 3.3 Matrix Theory: Some Basic Concepts; 3.3.1 Trace and Determinant of a Matrix 3.3.2 Eigenvalues and Eigenvectors of a Matrix3.4 Graph Matrices; 3.4.1 Adjacency Matrix; 3.4.2 Incidence Matrix; 3.4.3 Degree Matrix and Diffusion Matrix; 3.4.4 Laplace Matrix; 3.4.5 Cut-Set Matrix; 3.4.6 Path Matrix; 3.5 Spectral Graph Theory: Some Basic Results; 3.5.1 Spectral Characterization of Graph Connectivity; 3.5.1.1 Spectral Theory and Walks; 3.5.2 Spectral Characteristics of some Special Graphs and Subgraphs; 3.5.2.1 Tree; 3.5.2.2 Bipartite Graph; 3.5.2.3 Complete Graph; 3.5.2.4 Regular Graph; 3.5.2.5 Line Graph; 3.5.3 Spectral Theory and Graph Colouring 3.5.4 Spectral Theory and Graph Drawing |
| Record Nr. | UNINA-9910463483103321 |
| Weinheim, Germany : , : Wiley-VCH Verlang GmbH & Co. KGaA, , 2015 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Computational network theory : theoretical foundations and applications / / Edited by Matthias Dehmer, Frank Emmert-Streib, and Stefan Pickl
| Computational network theory : theoretical foundations and applications / / Edited by Matthias Dehmer, Frank Emmert-Streib, and Stefan Pickl |
| Pubbl/distr/stampa | Weinheim, Germany : , : Wiley-VCH Verlang GmbH & Co. KGaA, , 2015 |
| Descrizione fisica | 1 online resource (281 p.) |
| Disciplina | 006.3 |
| Collana | Quantitative and network biology |
| Soggetto topico |
Electronic commerce
Computational intelligence |
| ISBN |
3-527-69154-5
3-527-69151-0 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Cover; Title Page; Copyright; Dedication; Contents; Color Plates; Preface; List of Contributors; Chapter 1 Model Selection for Neural Network Models: A Statistical Perspective; 1.1 Introduction; 1.2 Feedforward Neural Network Models; 1.3 Model Selection; 1.3.1 Feature Selection by Relevance Measures; 1.3.2 Some Numerical Examples; 1.3.3 Application to Real Data; 1.4 The Selection of the Hidden Layer Size; 1.4.1 A Reality Check Approach; 1.4.2 Numerical Examples by Using the Reality Check; 1.4.3 Testing Superior Predictive Ability for Neural Network Modeling
1.4.4 Some Numerical Results Using Test of Superior Predictive Ability1.4.5 An Application to Real Data; 1.5 Concluding Remarks; References; Chapter 2 Measuring Structural Correlations in Graphs; 2.1 Introduction; 2.1.1 Solutions for Measuring Structural Correlations; 2.2 Related Work; 2.3 Self Structural Correlation; 2.3.1 Problem Formulation; 2.3.2 The Measure; 2.3.2.1 Random Walk and Hitting Time; 2.3.2.2 Decayed Hitting Time; 2.3.3 Computing Decayed Hitting Time; 2.3.3.1 Iterative Approximation; 2.3.3.2 A Sampling Algorithm for h(vi,B); 2.3.3.3 Complexity; 2.3.4 Assessing SSC 2.3.4.1 Estimating ρ (Vq)2.3.4.2 Estimating the Significance of ρ (Vq); 2.3.5 Empirical Studies; 2.3.5.1 Datasets; 2.3.5.2 Performance of DHT Approximation; 2.3.5.3 Effectiveness on Synthetic Events; 2.3.5.4 SSC of Real Event; 2.3.5.5 Scalability of Sampling-alg; 2.3.6 Discussions; 2.4 Two-Event Structural Correlation; 2.4.1 Preliminaries and Problem Formulation; 2.4.2 Measuring TESC; 2.4.2.1 The Test; 2.4.2.2 Reference Nodes; 2.4.3 Reference Node Sampling; 2.4.3.1 Batch_BFS; 2.4.3.2 Importance Sampling; 2.4.3.3 Global Sampling in Whole Graph; 2.4.3.4 Complexity Analysis; 2.4.4 Experiments 2.4.4.1 Graph Datasets2.4.4.2 Event Simulation Methodology; 2.4.4.3 Performance Comparison; 2.4.4.4 Batch Importance Sampling; 2.4.4.5 Impact of Graph Density; 2.4.4.6 Efficiency and Scalability; 2.4.4.7 Real Events; 2.4.5 Discussions; 2.5 Conclusions; Acknowledgments; References; Chapter 3 Spectral Graph Theory and Structural Analysis of Complex Networks: An Introduction; 3.1 Introduction; 3.2 Graph Theory: Some Basic Concepts; 3.2.1 Connectivity in Graphs; 3.2.2 Subgraphs and Special Graphs; 3.3 Matrix Theory: Some Basic Concepts; 3.3.1 Trace and Determinant of a Matrix 3.3.2 Eigenvalues and Eigenvectors of a Matrix3.4 Graph Matrices; 3.4.1 Adjacency Matrix; 3.4.2 Incidence Matrix; 3.4.3 Degree Matrix and Diffusion Matrix; 3.4.4 Laplace Matrix; 3.4.5 Cut-Set Matrix; 3.4.6 Path Matrix; 3.5 Spectral Graph Theory: Some Basic Results; 3.5.1 Spectral Characterization of Graph Connectivity; 3.5.1.1 Spectral Theory and Walks; 3.5.2 Spectral Characteristics of some Special Graphs and Subgraphs; 3.5.2.1 Tree; 3.5.2.2 Bipartite Graph; 3.5.2.3 Complete Graph; 3.5.2.4 Regular Graph; 3.5.2.5 Line Graph; 3.5.3 Spectral Theory and Graph Colouring 3.5.4 Spectral Theory and Graph Drawing |
| Record Nr. | UNINA-9910788289203321 |
| Weinheim, Germany : , : Wiley-VCH Verlang GmbH & Co. KGaA, , 2015 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
