Weighted Network Analysis: Applications in Genomics and Systems BiologySpringer Science & Business Media, 30.04.2011 - 421 Seiten High-throughput measurements of gene expression and genetic marker data facilitate systems biologic and systems genetic data analysis strategies. Gene co-expression networks have been used to study a variety of biological systems, bridging the gap from individual genes to biologically or clinically important emergent phenotypes. |
Inhalt
| 1 | |
Approximately Factorizable Networks | 35 |
Different Types of Network Concepts
| 45 |
Adjacency Functions and Their Topological Effects | 77 |
Correlation and Gene CoExpression Networks
| 91 |
Geometric Interpretation of Correlation Networks Using the Singular Value Decomposition
| 123 |
Constructing Networks from Matrices
| 161 |
Clustering Procedures and Module Detection
| 179 |
Association Measures and Statistical Significance Measures
| 249 |
Structural Equation Models and Directed Networks
| 279 |
Integrated Weighted Correlation Network Analysis of Mouse Liver Gene Expression Data
| 321 |
Networks Based on Regression Models and Prediction Methods
| 353 |
Networks Between Categorical or Discretized Numeric Variables
| 373 |
Network Based on the Joint Probability Distribution of Random Variables
| 401 |
| 413 | |
Evaluating Whether a Module is Preserved in Another Network
| 207 |
Andere Ausgaben - Alle anzeigen
Weighted Network Analysis: Applications in Genomics and Systems Biology Steve Horvath Keine Leseprobe verfügbar - 2014 |
Weighted Network Analysis: Applications in Genomics and Systems Biology Steve Horvath Keine Leseprobe verfügbar - 2011 |
Weighted Network Analysis: Applications in Genomics and Systems Biology Steve Horvath Keine Leseprobe verfügbar - 2011 |
Häufige Begriffe und Wortgruppen
adjacency function adjacency matrix algorithm Aoriginal approximate CF-based approximately factorizable blue module calculate causal anchors causal model CF-based analogs cluster tree clustering coefficient columns compute corresponding covariance data frame data set datX defined dendrogram denotes density described in Sect dimensional dissimilarity edge orienting eigenvector eigenvector-based estimate example Exercise regarding false discovery rate following R code fundamental network concepts gene co-expression network gene expression data gene significance Genomics hierarchical clustering Horvath hub gene hub node input intramodular connectivity Langfelder linear liver medianRank methods model fitting module eigengene module membership module preservation statistics mouse mutual information network analysis network modules node significance measure numeric vectors observed overlap matrix pairwise parameters permutation test q-value Rand index random variables regression model relationships scale-free topology shows simulated Springer Science+Business Media symmetric matrix test network unweighted weighted network WGCNA Z statistics Zhang Zsummary