Computational network analysis with R : applications in biology, medicine, and chemistry / edited by Mathtias Dehmer, Yongtang Shi, and Frank Emmert-Streib.

Format:

Book

Contributor:

Dehmer, Matthias, editor.

Shi, Yongtang, editor.

Emmert-Streib, Frank, editor.

Series:

Quantitative and network biology ; v. 7.

Quantitative and network biology ; volume 7

Language:

English

Subjects (All):

R (Computer program language).

Medicine--Computer programs.

Medicine.

Application software.

Physical Description:

1 online resource (365 p.)

Edition:

1st ed.

Place of Publication:

Weinheim : Wiley-VCH, [2017]

Summary:

This new title in the well-established "Quantitative Network Biology" series includes innovative and existing methods for analyzing network data in such areas as network biology and chemoinformatics. With its easy-to-follow introduction to the theoretical background and application-oriented chapters, the book demonstrates that R is a powerful language for statistically analyzing networks and for solving such large-scale phenomena as network sampling and bootstrapping. Written by editors and authors with an excellent track record in the field, this is the ultimate reference for R in Network Analysis.

Contents:

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)

Notes:

Includes index.

Description based on online resource; title from PDF title page (ebrary, viewed August 29, 2016).

ISBN:

9783527694372

3527694374

9783527694402

3527694404

9783527694365

3527694366

OCLC:

954474493