Applied statistics for network biology : methods in systems biology / edited by Matthias Dehmer, Frank Emmert-Streib, Armin Graber and Armindo Salvador

Includes bibliographical references and index.

Pt. One. Modeling, simulation, and meaning of gene networks: 1. Network analysis to interpret complex phenotypes -- 2. Stochastic modeling of gene regulatory networks -- 3. Modeling expression quantitative trait loci in multiple populations -- Pt. Two. Inference of gene networks: 4. Transcriptional network inference based on information theory -- 5. Elucidation of general and condition-dependent gene pathways using mixture models and bayesian networks -- 6. Multiscale network reconstruction from gene expression measurements: correlations, perturbations, and "A Priori Biological Knowledge" -- 7. Gene regulatory networks inference: combining a genetic programming and H [infinity symbol] filtering approach -- 8. Computational reconstruction of protein interaction networks -- Pt. Three. Analysis of gene networks: 9. What if the fit is unfit? Criteria for biological systems estimation beyond residual errors -- 10. Machine learning methods for identifying essential genes and proteins in networks -- 11. Gene coexpression networks for the analysis of DNA microarray data -- 12. Correlation network analysis and knowledge integration -- 13. Network screening: a new method to identify active networks from an ensemble of known networks -- 14. Community detection in biological networks -- 15. On some inverse problems in generating probabilistic boolean networks -- 16. Boolean analysis of gene expression datasets -- Pt. Four. Systems approach to diseases: 17. Representing cancer cell trajectories in a phase-space diagram: switching cellular states by biological phase transitions -- 18. Protein network analysis for disease gene identification and prioritization -- 19. Pathways and networks as functional descriptors for human disease and drug response endpoints.