Weighted Network Analysis: Applications in Genomics and Systems BiologyHigh-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. |
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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 allow analogs analysis anchors applications approach approximate assignment association assume brain calculate causal CF-based cluster co-expression coefficient color columns components compute conformity connectivity consider construction correlation network corresponding defined definition denotes density depends described dissimilarity distance distribution edge eigengene eigenvector elements equals estimate et al example Exercise factorizable female fitting function fundamental gene expression genetic given highly Horvath human implies indicates input interpretation intramodular linear maximum mean measure methods module module preservation mouse multiple mutual information network concepts nodes Note objects observed overlap parameters permutation plot preservation statistics random reference regarding regression relationships respectively sample satisfies scaled score Sect shows significance significance measure simulated single structural symmetric matrix threshold topological trait transformation tree variables vectors weighted Zsummary