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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 | ||
| ||
Graph polynomials / / [edited by] Yongtang Shi, Matthias Dehmer, Xueliang Li, Ivan Gutman
| Graph polynomials / / [edited by] Yongtang Shi, Matthias Dehmer, Xueliang Li, Ivan Gutman |
| Pubbl/distr/stampa | Boca Raton : , : CRC Press, , [2017] |
| Descrizione fisica | 1 online resource (262 pages) : illustrations |
| Disciplina | 511/.5 |
| Collana | Discrete Mathematics and Its Applications |
| Soggetto topico |
Graph theory
Combinatorial analysis Polynomials |
| ISBN |
1-315-35096-3
1-315-36799-8 1-4987-5591-7 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | 1. The Interlace Polynomial / Ada Morse -- 2. Independence Polynomials of k-Trees and Compound Graphs / William Staton and Bing Wei -- 3. New Aspects of the Abelian Sandpile Model on Graphs and Their Polynomials / Mark Dukes and Yvan Le Borgne -- 4. Second Quantization of Recurrences / Philip Feinsilver and John P. McSorley -- 5. A Survey on the Matching Polynomial / Ivan Gutman -- 6. On the Permanental Polynomials of Graphs / Wei Li, Shunyi Liu, Tingzeng Wu, and Heping Zhang -- 7. From the Ising and Potts Models to the General Graph Homomorphism Polynomial / Klas Markstrom -- 8. Derivatives and Real Roots of Graph Polynomials / Xueliang Li and Yongtang Shi -- 9. Logic-Based Computation of Graph Polynomials / Tomer Kotek -- 10. Alliance Polynomial / Walter Carballosa, Jose M. Rodriguez, Jose M. Sigarreta, and Yadira Torres-Nuez -- 11. Graph Polynomials and Set Functions / Bodo Lass. |
| Record Nr. | UNINA-9910153182003321 |
| Boca Raton : , : CRC Press, , [2017] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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